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59 Commits

Author SHA1 Message Date
Zhenchi
e46efb3d6c chore: bump version to 0.14.4
Signed-off-by: Zhenchi <zhongzc_arch@outlook.com>
2025-06-04 15:59:41 +08:00
Yingwen
34af9580e0 fix: do not accommodate fields for multi-value protocol (#6237) 2025-06-04 15:59:41 +08:00
Lei, HUANG
b19d23d665 fix(mito): revert initial builder capacity for TimeSeriesMemtable (#6231)
* fix/initial-builder-cap:
 ### Enhance Series Initialization and Capacity Management

 - **`simple_bulk_memtable.rs`**: Updated the `Series` initialization to use `with_capacity` with a specified capacity of 8192, improving memory management.
 - **`time_series.rs`**: Introduced `with_capacity` method in `Series` to allow custom initial capacity for `ValueBuilder`. Adjusted `INITIAL_BUILDER_CAPACITY` to 16 for more efficient memory usage. Added a new `new` method to maintain backward compatibility.

* fix/initial-builder-cap:
 ### Adjust Memory Allocation in Memtable

 - **`simple_bulk_memtable.rs`**: Reduced the initial capacity of `Series` from 8192 to 1024 to optimize memory usage.
 - **`time_series.rs`**: Decreased `INITIAL_BUILDER_CAPACITY` from 16 to 4 to improve efficiency in vector building.
2025-06-04 15:59:41 +08:00
dennis zhuang
209f15dd51 fix: set column index can't work in physical table (#6179) 2025-06-04 15:59:41 +08:00
discord9
0829fb204c chore: rm unnecessary depend for flow (#6047) 2025-06-04 15:59:41 +08:00
discord9
c8e470e8ed chore: upgrade hydroflow depend (#6011)
* chore: `hydroflow` -> `dfir`

* chore: refine log msg
2025-06-04 15:59:41 +08:00
Zhenchi
f66803622d chore: bump version to 0.14.3
Signed-off-by: Zhenchi <zhongzc_arch@outlook.com>
2025-05-23 20:23:23 +08:00
Ruihang Xia
e7774437b8 fix: require input ordering in series divide plan (#6148)
* require input ordering in series divide plan

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* add sqlness case

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* finilise

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

---------

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>
2025-05-23 20:23:23 +08:00
Ruihang Xia
c272b25456 feat: support altering multiple logical table in one remote write request (#6137)
Signed-off-by: Ruihang Xia <waynestxia@gmail.com>
2025-05-23 20:23:23 +08:00
discord9
724b802018 chore: invalid table flow mapping cache (#6135)
* chore: invalid table flow mapping

* chore: exists

* fix: invalid all related keys in kv cache when drop flow&refactor: per review

* fix: flow not found status code

* chore: rm unused error code

* chore: stuff

* chore: unused
2025-05-23 20:23:23 +08:00
Ruihang Xia
f3ca5f5d7f feat: accommodate default column name with pre-created table schema (#6126)
* refactor: prepare_mocked_backend

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* modify request in place

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* apply to influx line protocol

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* fix typo

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* return on empty alter expr list

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* expose to other write paths

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

---------

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>
2025-05-23 20:23:23 +08:00
Ruihang Xia
6c672b96bf fix: update promql-parser for regex anchor fix (#6117)
Signed-off-by: Ruihang Xia <waynestxia@gmail.com>
2025-05-23 20:23:23 +08:00
discord9
83018d6670 fix: flow update use proper update (#6108)
* fix: flow update use proper update

* refactor: per review

* fix: flow cache

* chore: per copilot review

* refactor: rm flow node id

* refactor: per review

* chore: per review

* refactor: per review

* chore: per review
2025-05-23 20:23:23 +08:00
discord9
69f1cbd484 fix(flow): flow task run interval (#6100)
* fix: always check for shutdown signal in flow
chore: correct log msg for flows that shouldn't exist
feat: use time window size/2 as sleep interval

* chore: better slower query refresh time

* chore

* refactor: per review
2025-05-23 20:23:23 +08:00
discord9
e1dad69648 fix: flownode chose fe randomly&not starve lock (#6077)
* fix: choose frontend randomly

* docs: update comment

* chore: more logs

* fix: ignore inserts until recovering flow is done

* chore: resolve TODO

* fix: rm unused code&set done in correct location

* refactor: speed up create flow
2025-05-23 20:23:23 +08:00
Ruihang Xia
6c976bc737 feat: don't hide atomic write dir (#6109)
* feat: don't hidden atomic write dir

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* compatible code

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* Update src/mito2/src/access_layer.rs

Co-authored-by: Yingwen <realevenyag@gmail.com>

---------

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>
Co-authored-by: Yingwen <realevenyag@gmail.com>
2025-05-23 20:23:23 +08:00
jeremyhi
b20c1ac797 chore: reduce unnecessary txns in alter operations (#6133) 2025-05-23 20:23:23 +08:00
Yingwen
d7cfb741a5 fix: clean files under the atomic write dir on failure (#6112)
* fix: remove files under atomic dir on failure

* fix: clean atomic dir on download failure

* chore: update comment

* fix: clean if failed to write without write cache

* feat: add a TempFileCleaner to clean files on failure

* chore: after merge fix

* chore: more fix

---------

Co-authored-by: discord9 <55937128+discord9@users.noreply.github.com>
Co-authored-by: discord9 <discord9@163.com>
2025-05-23 20:23:23 +08:00
Weny Xu
1b3efef15c fix: append noop entry when auto topic creation is disabled (#6092)
* feat: improve topic management and add stale records cleanup

* fix: fix unit tests

* chore: apply suggestions from CR

* chore: apply suggestions from CR
2025-05-23 20:23:23 +08:00
Yingwen
1ca2dbd240 fix: reset tags when creating an empty metric in prom call (#6056)
* Revert "chore: remove debug logs"

This reverts commit f73f3a7373c83db974d8ed80cb47f5f87317b490.

* chore: more logs

* fix: reset tags and fields

* test: add binary time fn test

* chore: remove logs

* test: sort result
2025-05-23 20:23:23 +08:00
Ning Sun
d596dba240 fix: ident value in set search_path (#6153)
* fix: ident value in set search_path

* refactor: remove unneeded clone
2025-05-23 20:23:23 +08:00
discord9
5c9cbb5f4c chore: bump version to 0.14.2 (#6032)
* chore: only retry when retry-able in flow (#5987)

* chore: only retry when retry-able

* chore: revert dbg change

* refactor: per review

* fix: check for available frontend first

* docs: more explain&longer timeout&feat: more retry at every level&try send select 1

* fix: use `sql` method for "SELECT 1"

* fix: also put recover flows in spawned task and a dead loop

* test: update transient error in flow rebuild test

* chore: sleep after sqlness sleep

* chore: add a warning

* chore: wait even more time after reboot

* fix: sanitize_connection_string (#6012)

* fix: disable recursion limit in prost (#6010)

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* ci: fix the bugs of release-dev-builder-images and add update-dev-builder-image-tag (#6009)

* fix: the dev-builder release job is not triggered by merged event

* ci: add update-dev-builder-image-tag

* fix: always create mito engine (#6018)

* fix: force streaming mode for instant source table (#6031)

* fix: force streaming mode for instant source table

* tests: sqlness test&refactor: get table

* refactor: per review

* chore: bump version to 0.14.2

---------

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>
Co-authored-by: jeremyhi <jiachun_feng@proton.me>
Co-authored-by: Ruihang Xia <waynestxia@gmail.com>
Co-authored-by: zyy17 <zyylsxm@gmail.com>
Co-authored-by: Lei, HUANG <6406592+v0y4g3r@users.noreply.github.com>
2025-05-01 09:20:01 -07:00
Zhenchi
e2df38d0d1 chore: bump version to 0.14.1 (#6006)
* feat: remove own greatest fn (#5994)

* fix: prune primary key with multiple columns may use default value as statistics (#5996)

* test: incorrect test result when filtering pk with multiple columns

* fix: prune non first tag correctly

Distinguish no column and no stats and only use default value when no
column

* test: update test result

* refactor: rename test file

* test: add test for null filter

* fix: use StatValues for null counts

* test: drop table

* test: fix unstable flow test

* fix: check if memtable is empty by stats (#5989)

fix/checking-memtable-empty-and-stats:
 - **Refactor timestamp updates**: Simplified timestamp range updates in `PartitionTreeMemtable` and `TimeSeriesMemtable` by replacing `update_timestamp_range` with `fetch_max` and `fetch_min` methods for `max_timestamp` and `min_timestamp`.
   - Affected files: `partition_tree.rs`, `time_series.rs`

 - **Remove unused code**: Deleted the `update_timestamp_range` method from `WriteMetrics` and removed unnecessary imports.
   - Affected file: `stats.rs`

 - **Optimize memtable filtering**: Streamlined the check for empty memtables in `ScanRegion` by directly using `time_range`.
   - Affected file: `scan_region.rs`

* chore: bump version to 0.14.1

Signed-off-by: Zhenchi <zhongzc_arch@outlook.com>

---------

Signed-off-by: Zhenchi <zhongzc_arch@outlook.com>
Co-authored-by: dennis zhuang <killme2008@gmail.com>
Co-authored-by: Yingwen <realevenyag@gmail.com>
Co-authored-by: Lei, HUANG <6406592+v0y4g3r@users.noreply.github.com>
2025-04-28 07:39:49 +00:00
discord9
66e2242e46 fix: conn timeout&refactor: better err msg (#5974)
* fix: conn timeout&refactor: better err msg

* chore: clippy

* chore: make test work

* chore: comment

* todo: fix null cast

* fix: retry conn&udd_calc

* chore: comment

* chore: apply suggestion

---------

Co-authored-by: dennis zhuang <killme2008@gmail.com>
2025-04-25 19:12:30 +00:00
Ning Sun
489b16ae30 fix: security update (#5982) 2025-04-25 18:11:09 +00:00
dennis zhuang
85d564b0fb fix: upgrade sqlparse and validate align in range query (#5958)
* fix: upgrade sqlparse and validate align in range query

* update sqlparser to the merged commit

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

---------

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>
Co-authored-by: Ruihang Xia <waynestxia@gmail.com>
Co-authored-by: Zhenchi <zhongzc_arch@outlook.com>
2025-04-25 17:34:49 +00:00
Zhenchi
d5026f3491 perf: optimize fulltext zh tokenizer for ascii-only text (#5975)
Signed-off-by: Zhenchi <zhongzc_arch@outlook.com>
2025-04-24 23:31:26 +00:00
Weny Xu
e30753fc31 feat: allow forced region failover for local WAL (#5972)
* feat: allow forced region failover for local WAL

* chore: upgrade config.md

* chore: apply suggestions from CR
2025-04-24 08:11:45 +00:00
Ruihang Xia
b476584f56 feat: remove hyper parameter from promql functions (#5955)
* quantile udaf

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* extrapolate rate

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* predict_linear, round, holt_winters, quantile_overtime

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* fix clippy

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* fix quantile function

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

---------

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>
2025-04-24 07:17:10 +00:00
Weny Xu
ff3a46b1d0 feat: improve observability of region migration procedure (#5967)
* feat: improve observability of region migration procedure

* chore: apply suggestions from CR

* chore: observe non-zero value
2025-04-24 04:00:14 +00:00
Weny Xu
a533ac2555 feat: enhance selector with node exclusion support (#5966) 2025-04-24 02:27:27 +00:00
dennis zhuang
cc5629b4a1 chore: remove coderabbit (#5969) 2025-04-24 02:15:44 +00:00
Weny Xu
f3d000f6ec feat: track region failover attempts and adjust timeout (#5952) 2025-04-23 18:19:18 +00:00
discord9
9557b76224 fix: try prune one less (#5965)
* try prune one less

* test: also not add one

* ci: use longer fuzz time

* revert fuzz time&per review

* chore: no (

* docs: add explain to offset used in delete records

* test: fix test_procedure_execution
2025-04-23 16:57:54 +00:00
discord9
a0900f5b90 feat(flow): use batching mode&fix sqlness (#5903)
* feat: use flow batching engine

broken: try using logical plan

fix: use dummy catalog for logical plan

fix: insert plan exec&sqlness grpc addr

feat: use frontend instance in flownode in standalone

feat: flow type in metasrv&fix: flush flow out of sync& column name alias

tests: sqlness update

tests: sqlness flow rebuild udpate

chore: per review

refactor: keep chnl mgr

refactor: use catalog mgr for get table

tests: use valid sql

fix: add more check

refactor: put flow type determine to frontend

* chore: update proto

* chore: update proto to main branch

* fix: add locks for create/drop flow&docs: update docs

* feat: flush_flow flush all ranges now

* test: add align time window test

* docs: explain `nodeid` use in check task

* refactor: AddAutoColumnRewriter check for Projection

* refactor: per review

* fix: query without time window also clean dirty time window

* chore: better logging

* chore: add comments per review

* refactor: per review

* chore: per review

* chore: per review rename args

* refactor: per review partially

* chore: update docs

* chore: use better error variant

* chore: better error variant

* refactor: rename FlowWorkerManager to FlowStreamingEngine

* rename again

* refactor: per review

* chore: rebase after #5963 merged

* refactor: rename all flow_worker_manager occurs

* docs: rm resolved TODO
2025-04-23 15:12:16 +00:00
Yingwen
45a05fb08c docs: fix some units and adds the opendal errors panel (#5962)
* docs: fixes units in the dashboard

* docs: add opendal errors panel

* docs: opendal traffic use decbytes

* docs: update readme

---------

Co-authored-by: zyy17 <zyylsxm@gmail.com>
2025-04-23 13:31:29 +00:00
LFC
71db79c8d6 feat: node excluder (#5964)
* feat: node excluder

* fix ci

* fix ci
2025-04-23 10:48:46 +00:00
discord9
79ed7bbc44 fix: store flow query ctx on creation (#5963)
* fix: store flow schema on creation

* chore: update sqlness

* refactor: save the entire query context to flow info

* chore: sqlness update

* chore: rm pub

* fix: keep old version compatibility
2025-04-23 09:59:09 +00:00
zyy17
02e9a66d7a chore: update dac tools image and docs (#5961) 2025-04-23 05:00:37 +00:00
Weny Xu
55cadcd2c0 feat: introduce flush metadata region task for metric engine (#5951)
* feat: introduce flush metadata region task for metric engine

* docs: generate config.md

* chore: add header

* test: fix unit test

* fix: fix unit tests

* chore: apply suggestions from CR

* chore: remove docs

* fix: fix unit tests
2025-04-23 04:51:22 +00:00
fys
8c4796734a chore: remove unused attribute (#5960) 2025-04-23 03:17:13 +00:00
Yuhan Wang
919956999b fix: use max in flushed entry id and topic latest entry id (#5946) 2025-04-22 23:48:32 +00:00
ZonaHe
7e5f6cbeae feat: update dashboard to v0.9.0 (#5948)
Co-authored-by: ZonaHex <ZonaHex@users.noreply.github.com>
2025-04-22 11:35:33 +00:00
shuiyisong
5c07f0dec7 refactor: run_pipeline parameters (#5954)
* refactor: simplify run_pipeline params

* refactor: remove unnecessory function wrap
2025-04-22 11:34:19 +00:00
discord9
9fb0487e67 fix: parse flow expire after interval (#5953)
* fix: parse flow expire after interval

* fix: correct 30.44&comments
2025-04-22 08:44:04 +00:00
discord9
6e407ae4b9 test: use random seed for window sort fuzz test (#5950)
tests: use random seed for window sort fuzz test
2025-04-22 08:14:27 +00:00
Ning Sun
bcefc6b83f feat: add format support for promql http api (not prometheus) (#5939)
* feat: add format support for promql http api (not prometheus)

* test: add csv format test
2025-04-22 08:10:35 +00:00
Weny Xu
0f77135ef9 feat: add exclude_peer_ids to SelectorOptions (#5949)
* feat: add `exclude_peer_ids` to `SelectorOptions`

* chore: apply suggestions from CR

* fix: clippy
2025-04-22 07:49:22 +00:00
Weny Xu
0a4594c9e2 fix: remove obsolete failover detectors after region leader change (#5944)
* fix: remove obsolete failover detectors after region leader change

* chore: apply suggestions from CR

* fix: fix unit tests

* fix: fix unit test

* fix: failover logic
2025-04-22 06:15:47 +00:00
LFC
d9437c6da7 chore: assert plugin uniqueness (#5947) 2025-04-22 06:04:06 +00:00
zyy17
35f4fa3c3e refactor: unify all dashboards and use dac tool to generate intermediate dashboards (#5933)
* refactor: split cluster metrics into multiple dashboards

* chore: merge multiple dashboards into one dashboard

* refactor: add 'dac' tool to generate a intermediate dashboards

* refactor: generate markdown docs for dashboards
2025-04-22 06:03:01 +00:00
jeremyhi
60e4607b64 chore: better buckets for heartbeat stat size histogram (#5945)
chore: better buckets for METRIC_META_HEARTBEAT_STAT_MEMORY_SIZE
2025-04-21 16:12:27 +00:00
shuiyisong
3b8c6d5ce3 chore: use once_cell to avoid parse everytime in pipeline exec (#5943)
* chore: use once_cell to avoid parse everytime

* chore: remove pub on options
2025-04-21 12:55:48 +00:00
Weny Xu
7a8e1bc3f9 feat: support building metasrv with selector from plugins (#5942)
* chore: expose selector

* feat: use f64

* chore: expose selector::common

* feat: build metasrv with selector from plugins
2025-04-21 10:59:24 +00:00
Yuhan Wang
ee07b9bfa8 test: update configs to enable auto wal prune (#5938)
* test: update configs to enable auto wal prune

* fix: add humantime_serde

* fix: enable overwrite_entry_start_id

* fix: not in metasrv

* chore: update default value name

* Apply suggestions from code review

Co-authored-by: jeremyhi <jiachun_feng@proton.me>

* fix: kafka use overwrite_entry_start_id

---------

Co-authored-by: jeremyhi <jiachun_feng@proton.me>
2025-04-21 07:57:43 +00:00
Lei, HUANG
90ffaa8a62 feat: implement otel-arrow protocol for GreptimeDB (#5840)
* [wip]: implement arrow service

* add service

* feat/otel-arrow:
 ### Add OpenTelemetry Arrow Support

 - **`Cargo.toml`, `Cargo.lock`**: Updated `otel-arrow-rust` dependency to use a local path and added `arrow-ipc` as a dependency.
 - **`src/servers/src/grpc.rs`, `src/servers/src/grpc/builder.rs`**: Integrated `ArrowMetricsServiceServer` with gRPC server, including support for custom header interception and message compression.
 - **`src/servers/src/otel_arrow.rs`**: Implemented `OtelArrowServiceHandler` for handling OpenTelemetry Arrow metrics and added `HeaderInterceptor` for custom header handling.

* feat/otel-arrow:
 Add error handling for OpenTelemetry Arrow requests

 - **`src/error.rs`**: Introduced a new error variant `HandleOtelArrowRequest` to handle failures in processing OpenTelemetry Arrow requests.
 - **`src/otel_arrow.rs`**: Implemented error handling for receiving and consuming batches from the OpenTelemetry Arrow client. Added logging for errors and updated the response status accordingly.

* feat/otel-arrow:
 Remove `otel_arrow` Module from gRPC Server

 - Deleted the `otel_arrow` module from the gRPC server implementation.
 - Removed the `otel_arrow` module import from `grpc.rs`.
 - Deleted the `otel_arrow.rs` file, which contained the `OtelArrowServer` struct and its implementation.

* feat/otel-arrow:
 ## Remove `Arc` Implementations for Protocol and Pipeline Handlers

 - **Removed `Arc` Implementations**: Deleted `Arc` implementations for `OpenTelemetryProtocolHandler` and `PipelineHandler` traits in `query_handler.rs`. This change simplifies the code by removing redundant async trait implementations for `Arc<T>`.
 - **File Affected**: `src/servers/src/query_handler.rs`

* feat/otel-arrow:
 Improve error handling and metadata processing in `otel_arrow.rs`

 - Updated error handling by ignoring the result of `sender.send` to prevent panic on failure.
 - Enhanced metadata processing in `HeaderInterceptor` by using `Ok` to safely handle `grpc-encoding` entry retrieval.

* fix dependency

* feat/otel-arrow:
 - **Update Dependencies**:
   - Moved `otel-arrow-rust` dependency in `Cargo.toml`.
   - Adjusted workspace dependencies in `src/frontend/Cargo.toml`.

 - **Error Handling**:
   - Removed `MissingQueryContext` error variant from `src/servers/src/error.rs`.

* fix: toml format

* remove useless code

* chore: resolve conflicts
2025-04-21 07:24:23 +00:00
Yingwen
56f319a707 fix: filter doesn't consider default values after schema change (#5912)
* test: sqlness test case

* feat: use correct default while pruning row groups

* fix: consider default in SimpleFilterContext

* test: update sqlness test

* test: add order by
2025-04-21 06:32:26 +00:00
shuiyisong
9df493988b fix: wrong error msg in pipeline (#5937) 2025-04-21 04:05:46 +00:00
dennis zhuang
ad1b77ab04 feat: update readme (#5936)
* fix: title

* chore: format

* chore: format

* chore: format
2025-04-21 02:44:44 +00:00
287 changed files with 25324 additions and 15836 deletions

View File

@@ -1,15 +0,0 @@
# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json
language: "en-US"
early_access: false
reviews:
profile: "chill"
request_changes_workflow: false
high_level_summary: true
poem: true
review_status: true
collapse_walkthrough: false
auto_review:
enabled: false
drafts: false
chat:
auto_reply: true

View File

@@ -7,7 +7,8 @@ meta:
provider = "kafka"
broker_endpoints = ["kafka.kafka-cluster.svc.cluster.local:9092"]
num_topics = 3
auto_prune_topic_records = true
auto_prune_interval = "30s"
trigger_flush_threshold = 100
[datanode]
[datanode.client]
@@ -22,6 +23,7 @@ datanode:
provider = "kafka"
broker_endpoints = ["kafka.kafka-cluster.svc.cluster.local:9092"]
linger = "2ms"
overwrite_entry_start_id = true
frontend:
configData: |-
[runtime]

37
.github/scripts/update-dev-builder-version.sh vendored Executable file
View File

@@ -0,0 +1,37 @@
#!/bin/bash
DEV_BUILDER_IMAGE_TAG=$1
update_dev_builder_version() {
if [ -z "$DEV_BUILDER_IMAGE_TAG" ]; then
echo "Error: Should specify the dev-builder image tag"
exit 1
fi
# Configure Git configs.
git config --global user.email greptimedb-ci@greptime.com
git config --global user.name greptimedb-ci
# Checkout a new branch.
BRANCH_NAME="ci/update-dev-builder-$(date +%Y%m%d%H%M%S)"
git checkout -b $BRANCH_NAME
# Update the dev-builder image tag in the Makefile.
gsed -i "s/DEV_BUILDER_IMAGE_TAG ?=.*/DEV_BUILDER_IMAGE_TAG ?= ${DEV_BUILDER_IMAGE_TAG}/g" Makefile
# Commit the changes.
git add Makefile
git commit -m "ci: update dev-builder image tag"
git push origin $BRANCH_NAME
# Create a Pull Request.
gh pr create \
--title "ci: update dev-builder image tag" \
--body "This PR updates the dev-builder image tag" \
--base main \
--head $BRANCH_NAME \
--reviewer zyy17 \
--reviewer daviderli614
}
update_dev_builder_version

View File

@@ -21,32 +21,6 @@ jobs:
run: sudo apt-get install -y jq
# Make the check.sh script executable
- name: Make check.sh executable
run: chmod +x grafana/check.sh
# Run the check.sh script
- name: Run check.sh
run: ./grafana/check.sh
# Only run summary.sh for pull_request events (not for merge queues or final pushes)
- name: Check if this is a pull request
id: check-pr
- name: Check grafana dashboards
run: |
if [[ "${{ github.event_name }}" == "pull_request" ]]; then
echo "is_pull_request=true" >> $GITHUB_OUTPUT
else
echo "is_pull_request=false" >> $GITHUB_OUTPUT
fi
# Make the summary.sh script executable
- name: Make summary.sh executable
if: steps.check-pr.outputs.is_pull_request == 'true'
run: chmod +x grafana/summary.sh
# Run the summary.sh script and add its output to the GitHub Job Summary
- name: Run summary.sh and add to Job Summary
if: steps.check-pr.outputs.is_pull_request == 'true'
run: |
SUMMARY=$(./grafana/summary.sh)
echo "### Summary of Grafana Panels" >> $GITHUB_STEP_SUMMARY
echo "$SUMMARY" >> $GITHUB_STEP_SUMMARY
make check-dashboards

View File

@@ -24,11 +24,19 @@ on:
description: Release dev-builder-android image
required: false
default: false
update_dev_builder_image_tag:
type: boolean
description: Update the DEV_BUILDER_IMAGE_TAG in Makefile and create a PR
required: false
default: false
jobs:
release-dev-builder-images:
name: Release dev builder images
if: ${{ inputs.release_dev_builder_ubuntu_image || inputs.release_dev_builder_centos_image || inputs.release_dev_builder_android_image }} # Only manually trigger this job.
# The jobs are triggered by the following events:
# 1. Manually triggered workflow_dispatch event
# 2. Push event when the PR that modifies the `rust-toolchain.toml` or `docker/dev-builder/**` is merged to main
if: ${{ github.event_name == 'push' || inputs.release_dev_builder_ubuntu_image || inputs.release_dev_builder_centos_image || inputs.release_dev_builder_android_image }}
runs-on: ubuntu-latest
outputs:
version: ${{ steps.set-version.outputs.version }}
@@ -57,9 +65,9 @@ jobs:
version: ${{ env.VERSION }}
dockerhub-image-registry-username: ${{ secrets.DOCKERHUB_USERNAME }}
dockerhub-image-registry-token: ${{ secrets.DOCKERHUB_TOKEN }}
build-dev-builder-ubuntu: ${{ inputs.release_dev_builder_ubuntu_image }}
build-dev-builder-centos: ${{ inputs.release_dev_builder_centos_image }}
build-dev-builder-android: ${{ inputs.release_dev_builder_android_image }}
build-dev-builder-ubuntu: ${{ inputs.release_dev_builder_ubuntu_image || github.event_name == 'push' }}
build-dev-builder-centos: ${{ inputs.release_dev_builder_centos_image || github.event_name == 'push' }}
build-dev-builder-android: ${{ inputs.release_dev_builder_android_image || github.event_name == 'push' }}
release-dev-builder-images-ecr:
name: Release dev builder images to AWS ECR
@@ -85,7 +93,7 @@ jobs:
- name: Push dev-builder-ubuntu image
shell: bash
if: ${{ inputs.release_dev_builder_ubuntu_image }}
if: ${{ inputs.release_dev_builder_ubuntu_image || github.event_name == 'push' }}
env:
IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -106,7 +114,7 @@ jobs:
- name: Push dev-builder-centos image
shell: bash
if: ${{ inputs.release_dev_builder_centos_image }}
if: ${{ inputs.release_dev_builder_centos_image || github.event_name == 'push' }}
env:
IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -127,7 +135,7 @@ jobs:
- name: Push dev-builder-android image
shell: bash
if: ${{ inputs.release_dev_builder_android_image }}
if: ${{ inputs.release_dev_builder_android_image || github.event_name == 'push' }}
env:
IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -162,7 +170,7 @@ jobs:
- name: Push dev-builder-ubuntu image
shell: bash
if: ${{ inputs.release_dev_builder_ubuntu_image }}
if: ${{ inputs.release_dev_builder_ubuntu_image || github.event_name == 'push' }}
env:
IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -176,7 +184,7 @@ jobs:
- name: Push dev-builder-centos image
shell: bash
if: ${{ inputs.release_dev_builder_centos_image }}
if: ${{ inputs.release_dev_builder_centos_image || github.event_name == 'push' }}
env:
IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -190,7 +198,7 @@ jobs:
- name: Push dev-builder-android image
shell: bash
if: ${{ inputs.release_dev_builder_android_image }}
if: ${{ inputs.release_dev_builder_android_image || github.event_name == 'push' }}
env:
IMAGE_VERSION: ${{ needs.release-dev-builder-images.outputs.version }}
IMAGE_NAMESPACE: ${{ vars.IMAGE_NAMESPACE }}
@@ -201,3 +209,24 @@ jobs:
quay.io/skopeo/stable:latest \
copy -a docker://docker.io/$IMAGE_NAMESPACE/dev-builder-android:$IMAGE_VERSION \
docker://$ACR_IMAGE_REGISTRY/$IMAGE_NAMESPACE/dev-builder-android:$IMAGE_VERSION
update-dev-builder-image-tag:
name: Update dev-builder image tag
runs-on: ubuntu-latest
permissions:
contents: write
pull-requests: write
if: ${{ github.event_name == 'push' || inputs.update_dev_builder_image_tag }}
needs: [
release-dev-builder-images
]
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Update dev-builder image tag
shell: bash
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: |
./.github/scripts/update-dev-builder-version.sh ${{ needs.release-dev-builder-images.outputs.version }}

1402
Cargo.lock generated

File diff suppressed because it is too large Load Diff

View File

@@ -68,7 +68,7 @@ members = [
resolver = "2"
[workspace.package]
version = "0.14.0"
version = "0.14.4"
edition = "2021"
license = "Apache-2.0"
@@ -112,15 +112,15 @@ clap = { version = "4.4", features = ["derive"] }
config = "0.13.0"
crossbeam-utils = "0.8"
dashmap = "6.1"
datafusion = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "5bbedc6704162afb03478f56ffb629405a4e1220" }
datafusion-common = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "5bbedc6704162afb03478f56ffb629405a4e1220" }
datafusion-expr = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "5bbedc6704162afb03478f56ffb629405a4e1220" }
datafusion-functions = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "5bbedc6704162afb03478f56ffb629405a4e1220" }
datafusion-optimizer = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "5bbedc6704162afb03478f56ffb629405a4e1220" }
datafusion-physical-expr = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "5bbedc6704162afb03478f56ffb629405a4e1220" }
datafusion-physical-plan = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "5bbedc6704162afb03478f56ffb629405a4e1220" }
datafusion-sql = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "5bbedc6704162afb03478f56ffb629405a4e1220" }
datafusion-substrait = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "5bbedc6704162afb03478f56ffb629405a4e1220" }
datafusion = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
datafusion-common = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
datafusion-expr = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
datafusion-functions = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
datafusion-optimizer = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
datafusion-physical-expr = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
datafusion-physical-plan = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
datafusion-sql = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
datafusion-substrait = { git = "https://github.com/waynexia/arrow-datafusion.git", rev = "e104c7cf62b11dd5fe41461b82514978234326b4" }
deadpool = "0.12"
deadpool-postgres = "0.14"
derive_builder = "0.20"
@@ -129,7 +129,7 @@ etcd-client = "0.14"
fst = "0.4.7"
futures = "0.3"
futures-util = "0.3"
greptime-proto = { git = "https://github.com/GreptimeTeam/greptime-proto.git", rev = "b6d9cffd43c4e6358805a798f17e03e232994b82" }
greptime-proto = { git = "https://github.com/GreptimeTeam/greptime-proto.git", rev = "4d4136692fe7fbbd509ebc8c902f6afcc0ce61e4" }
hex = "0.4"
http = "1"
humantime = "2.1"
@@ -161,8 +161,10 @@ parquet = { version = "54.2", default-features = false, features = ["arrow", "as
paste = "1.0"
pin-project = "1.0"
prometheus = { version = "0.13.3", features = ["process"] }
promql-parser = { version = "0.5.1", features = ["ser"] }
prost = "0.13"
promql-parser = { git = "https://github.com/GreptimeTeam/promql-parser.git", rev = "0410e8b459dda7cb222ce9596f8bf3971bd07bd2", features = [
"ser",
] }
prost = { version = "0.13", features = ["no-recursion-limit"] }
raft-engine = { version = "0.4.1", default-features = false }
rand = "0.9"
ratelimit = "0.10"
@@ -191,7 +193,7 @@ simd-json = "0.15"
similar-asserts = "1.6.0"
smallvec = { version = "1", features = ["serde"] }
snafu = "0.8"
sqlparser = { git = "https://github.com/GreptimeTeam/sqlparser-rs.git", rev = "e98e6b322426a9d397a71efef17075966223c089", features = [
sqlparser = { git = "https://github.com/GreptimeTeam/sqlparser-rs.git", rev = "0cf6c04490d59435ee965edd2078e8855bd8471e", features = [
"visitor",
"serde",
] } # branch = "v0.54.x"
@@ -269,6 +271,9 @@ metric-engine = { path = "src/metric-engine" }
mito2 = { path = "src/mito2" }
object-store = { path = "src/object-store" }
operator = { path = "src/operator" }
otel-arrow-rust = { git = "https://github.com/open-telemetry/otel-arrow", rev = "5d551412d2a12e689cde4d84c14ef29e36784e51", features = [
"server",
] }
partition = { path = "src/partition" }
pipeline = { path = "src/pipeline" }
plugins = { path = "src/plugins" }

View File

@@ -222,6 +222,16 @@ start-cluster: ## Start the greptimedb cluster with etcd by using docker compose
stop-cluster: ## Stop the greptimedb cluster that created by docker compose.
docker compose -f ./docker/docker-compose/cluster-with-etcd.yaml stop
##@ Grafana
.PHONY: check-dashboards
check-dashboards: ## Check the Grafana dashboards.
@./grafana/scripts/check.sh
.PHONY: dashboards
dashboards: ## Generate the Grafana dashboards for standalone mode and intermediate dashboards.
@./grafana/scripts/gen-dashboards.sh
##@ Docs
config-docs: ## Generate configuration documentation from toml files.
docker run --rm \

View File

@@ -6,7 +6,7 @@
</picture>
</p>
<h2 align="center">Unified & Cost-Effective Observability Database for Metrics, Logs, and Events</h2>
<h2 align="center">Real-Time & Cloud-Native Observability Database<br/>for metrics, logs, and traces</h2>
<div align="center">
<h3 align="center">

View File

@@ -319,6 +319,7 @@
| `selector` | String | `round_robin` | Datanode selector type.<br/>- `round_robin` (default value)<br/>- `lease_based`<br/>- `load_based`<br/>For details, please see "https://docs.greptime.com/developer-guide/metasrv/selector". |
| `use_memory_store` | Bool | `false` | Store data in memory. |
| `enable_region_failover` | Bool | `false` | Whether to enable region failover.<br/>This feature is only available on GreptimeDB running on cluster mode and<br/>- Using Remote WAL<br/>- Using shared storage (e.g., s3). |
| `allow_region_failover_on_local_wal` | Bool | `false` | Whether to allow region failover on local WAL.<br/>**This option is not recommended to be set to true, because it may lead to data loss during failover.** |
| `node_max_idle_time` | String | `24hours` | Max allowed idle time before removing node info from metasrv memory. |
| `enable_telemetry` | Bool | `true` | Whether to enable greptimedb telemetry. Enabled by default. |
| `runtime` | -- | -- | The runtime options. |

View File

@@ -50,6 +50,10 @@ use_memory_store = false
## - Using shared storage (e.g., s3).
enable_region_failover = false
## Whether to allow region failover on local WAL.
## **This option is not recommended to be set to true, because it may lead to data loss during failover.**
allow_region_failover_on_local_wal = false
## Max allowed idle time before removing node info from metasrv memory.
node_max_idle_time = "24hours"

View File

@@ -1,61 +1,89 @@
Grafana dashboard for GreptimeDB
--------------------------------
# Grafana dashboards for GreptimeDB
GreptimeDB's official Grafana dashboard.
## Overview
Status notify: we are still working on this config. It's expected to change frequently in the recent days. Please feel free to submit your feedback and/or contribution to this dashboard 🤗
This repository maintains the Grafana dashboards for GreptimeDB. It has two types of dashboards:
If you use Helm [chart](https://github.com/GreptimeTeam/helm-charts) to deploy GreptimeDB cluster, you can enable self-monitoring by setting the following values in your Helm chart:
- `cluster/dashboard.json`: The Grafana dashboard for the GreptimeDB cluster. Read the [dashboard.md](./dashboards/cluster/dashboard.md) for more details.
- `standalone/dashboard.json`: The Grafana dashboard for the standalone GreptimeDB instance. **It's generated from the `cluster/dashboard.json` by removing the instance filter through the `make dashboards` command**. Read the [dashboard.md](./dashboards/standalone/dashboard.md) for more details.
As the rapid development of GreptimeDB, the metrics may be changed, and please feel free to submit your feedback and/or contribution to this dashboard 🤗
**NOTE**:
- The Grafana version should be greater than 9.0.
- If you want to modify the dashboards, you only need to modify the `cluster/dashboard.json` and run the `make dashboards` command to generate the `standalone/dashboard.json` and other related files.
To maintain the dashboards easily, we use the [`dac`](https://github.com/zyy17/dac) tool to generate the intermediate dashboards and markdown documents:
- `cluster/dashboard.yaml`: The intermediate dashboard for the GreptimeDB cluster.
- `standalone/dashboard.yaml`: The intermediate dashboard for the standalone GreptimeDB instance.
## Data Sources
There are two data sources for the dashboards to fetch the metrics:
- **Prometheus**: Expose the metrics of GreptimeDB.
- **Information Schema**: It is the MySQL port of the current monitored instance. The `overview` dashboard will use this datasource to show the information schema of the current instance.
## Instance Filters
To deploy the dashboards for multiple scenarios (K8s, bare metal, etc.), we prefer to use the `instance` label when filtering instances.
Additionally, we recommend including the `pod` label in the legend to make it easier to identify each instance, even though this field will be empty in bare metal scenarios.
For example, the following query is recommended:
```promql
sum(process_resident_memory_bytes{instance=~"$datanode"}) by (instance, pod)
```
And the legend will be like: `[{{instance}}]-[{{ pod }}]`.
## Deployment
### Helm
If you use the Helm [chart](https://github.com/GreptimeTeam/helm-charts) to deploy a GreptimeDB cluster, you can enable self-monitoring by setting the following values in your Helm chart:
- `monitoring.enabled=true`: Deploys a standalone GreptimeDB instance dedicated to monitoring the cluster;
- `grafana.enabled=true`: Deploys Grafana and automatically imports the monitoring dashboard;
The standalone GreptimeDB instance will collect metrics from your cluster and the dashboard will be available in the Grafana UI. For detailed deployment instructions, please refer to our [Kubernetes deployment guide](https://docs.greptime.com/nightly/user-guide/deployments/deploy-on-kubernetes/getting-started).
The standalone GreptimeDB instance will collect metrics from your cluster, and the dashboard will be available in the Grafana UI. For detailed deployment instructions, please refer to our [Kubernetes deployment guide](https://docs.greptime.com/nightly/user-guide/deployments/deploy-on-kubernetes/getting-started).
# How to use
### Self-host Prometheus and import dashboards manually
## `greptimedb.json`
1. **Configure Prometheus to scrape the cluster**
Open Grafana Dashboard page, choose `New` -> `Import`. And upload `greptimedb.json` file.
The following is an example configuration(**Please modify it according to your actual situation**):
## `greptimedb-cluster.json`
```yml
# example config
# only to indicate how to assign labels to each target
# modify yours accordingly
scrape_configs:
- job_name: metasrv
static_configs:
- targets: ['<metasrv-ip>:<port>']
This cluster dashboard provides a comprehensive view of incoming requests, response statuses, and internal activities such as flush and compaction, with a layered structure from frontend to datanode. Designed with a focus on alert functionality, its primary aim is to highlight any anomalies in metrics, allowing users to quickly pinpoint the cause of errors.
- job_name: datanode
static_configs:
- targets: ['<datanode0-ip>:<port>', '<datanode1-ip>:<port>', '<datanode2-ip>:<port>']
We use Prometheus to scrape off metrics from nodes in GreptimeDB cluster, Grafana to visualize the diagram. Any compatible stack should work too.
- job_name: frontend
static_configs:
- targets: ['<frontend-ip>:<port>']
```
__Note__: This dashboard is still in an early stage of development. Any issue or advice on improvement is welcomed.
2. **Configure the data sources in Grafana**
### Configuration
You need to add two data sources in Grafana:
Please ensure the following configuration before importing the dashboard into Grafana.
- Prometheus: It is the Prometheus instance that scrapes the GreptimeDB metrics.
- Information Schema: It is the MySQL port of the current monitored instance. The dashboard will use this datasource to show the information schema of the current instance.
__1. Prometheus scrape config__
3. **Import the dashboards based on your deployment scenario**
Configure Prometheus to scrape the cluster.
```yml
# example config
# only to indicate how to assign labels to each target
# modify yours accordingly
scrape_configs:
- job_name: metasrv
static_configs:
- targets: ['<metasrv-ip>:<port>']
- job_name: datanode
static_configs:
- targets: ['<datanode0-ip>:<port>', '<datanode1-ip>:<port>', '<datanode2-ip>:<port>']
- job_name: frontend
static_configs:
- targets: ['<frontend-ip>:<port>']
```
__2. Grafana config__
Create a Prometheus data source in Grafana before using this dashboard. We use `datasource` as a variable in Grafana dashboard so that multiple environments are supported.
### Usage
Use `datasource` or `instance` on the upper-left corner to filter data from certain node.
- **Cluster**: Import the `cluster/dashboard.json` dashboard.
- **Standalone**: Import the `standalone/dashboard.json` dashboard.

View File

@@ -1,19 +0,0 @@
#!/usr/bin/env bash
BASEDIR=$(dirname "$0")
# Use jq to check for panels with empty or missing descriptions
invalid_panels=$(cat $BASEDIR/greptimedb-cluster.json | jq -r '
.panels[]
| select((.type == "stats" or .type == "timeseries") and (.description == "" or .description == null))
')
# Check if any invalid panels were found
if [[ -n "$invalid_panels" ]]; then
echo "Error: The following panels have empty or missing descriptions:"
echo "$invalid_panels"
exit 1
else
echo "All panels with type 'stats' or 'timeseries' have valid descriptions."
exit 0
fi

File diff suppressed because it is too large Load Diff

View File

@@ -0,0 +1,97 @@
# Overview
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Uptime | `time() - process_start_time_seconds` | `stat` | The start time of GreptimeDB. | `prometheus` | `s` | `__auto` |
| Version | `SELECT pkg_version FROM information_schema.build_info` | `stat` | GreptimeDB version. | `mysql` | -- | -- |
| Total Ingestion Rate | `sum(rate(greptime_table_operator_ingest_rows[$__rate_interval]))` | `stat` | Total ingestion rate. | `prometheus` | `rowsps` | `__auto` |
| Total Storage Size | `select SUM(disk_size) from information_schema.region_statistics;` | `stat` | Total number of data file size. | `mysql` | `decbytes` | -- |
| Total Rows | `select SUM(region_rows) from information_schema.region_statistics;` | `stat` | Total number of data rows in the cluster. Calculated by sum of rows from each region. | `mysql` | `sishort` | -- |
| Deployment | `SELECT count(*) as datanode FROM information_schema.cluster_info WHERE peer_type = 'DATANODE';`<br/>`SELECT count(*) as frontend FROM information_schema.cluster_info WHERE peer_type = 'FRONTEND';`<br/>`SELECT count(*) as metasrv FROM information_schema.cluster_info WHERE peer_type = 'METASRV';`<br/>`SELECT count(*) as flownode FROM information_schema.cluster_info WHERE peer_type = 'FLOWNODE';` | `stat` | The deployment topology of GreptimeDB. | `mysql` | -- | -- |
| Database Resources | `SELECT COUNT(*) as databases FROM information_schema.schemata WHERE schema_name NOT IN ('greptime_private', 'information_schema')`<br/>`SELECT COUNT(*) as tables FROM information_schema.tables WHERE table_schema != 'information_schema'`<br/>`SELECT COUNT(region_id) as regions FROM information_schema.region_peers`<br/>`SELECT COUNT(*) as flows FROM information_schema.flows` | `stat` | The number of the key resources in GreptimeDB. | `mysql` | -- | -- |
| Data Size | `SELECT SUM(memtable_size) * 0.42825 as WAL FROM information_schema.region_statistics;`<br/>`SELECT SUM(index_size) as index FROM information_schema.region_statistics;`<br/>`SELECT SUM(manifest_size) as manifest FROM information_schema.region_statistics;` | `stat` | The data size of wal/index/manifest in the GreptimeDB. | `mysql` | `decbytes` | -- |
# Ingestion
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Total Ingestion Rate | `sum(rate(greptime_table_operator_ingest_rows{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | Total ingestion rate.<br/><br/>Here we listed 3 primary protocols:<br/><br/>- Prometheus remote write<br/>- Greptime's gRPC API (when using our ingest SDK)<br/>- Log ingestion http API<br/> | `prometheus` | `rowsps` | `ingestion` |
| Ingestion Rate by Type | `sum(rate(greptime_servers_http_logs_ingestion_counter[$__rate_interval]))`<br/>`sum(rate(greptime_servers_prometheus_remote_write_samples[$__rate_interval]))` | `timeseries` | Total ingestion rate.<br/><br/>Here we listed 3 primary protocols:<br/><br/>- Prometheus remote write<br/>- Greptime's gRPC API (when using our ingest SDK)<br/>- Log ingestion http API<br/> | `prometheus` | `rowsps` | `http-logs` |
# Queries
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Total Query Rate | `sum (rate(greptime_servers_mysql_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))`<br/>`sum (rate(greptime_servers_postgres_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))`<br/>`sum (rate(greptime_servers_http_promql_elapsed_counte{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | Total rate of query API calls by protocol. This metric is collected from frontends.<br/><br/>Here we listed 3 main protocols:<br/>- MySQL<br/>- Postgres<br/>- Prometheus API<br/><br/>Note that there are some other minor query APIs like /sql are not included | `prometheus` | `reqps` | `mysql` |
# Resources
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Datanode Memory per Instance | `sum(process_resident_memory_bytes{instance=~"$datanode"}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{instance}}]-[{{ pod }}]` |
| Datanode CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{instance=~"$datanode"}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]` |
| Frontend Memory per Instance | `sum(process_resident_memory_bytes{instance=~"$frontend"}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{ instance }}]-[{{ pod }}]` |
| Frontend CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{instance=~"$frontend"}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]-cpu` |
| Metasrv Memory per Instance | `sum(process_resident_memory_bytes{instance=~"$metasrv"}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{ instance }}]-[{{ pod }}]-resident` |
| Metasrv CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{instance=~"$metasrv"}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]` |
| Flownode Memory per Instance | `sum(process_resident_memory_bytes{instance=~"$flownode"}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{ instance }}]-[{{ pod }}]` |
| Flownode CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{instance=~"$flownode"}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]` |
# Frontend Requests
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| HTTP QPS per Instance | `sum by(instance, pod, path, method, code) (rate(greptime_servers_http_requests_elapsed_count{instance=~"$frontend",path!~"/health\|/metrics"}[$__rate_interval]))` | `timeseries` | HTTP QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]` |
| HTTP P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, path, method, code) (rate(greptime_servers_http_requests_elapsed_bucket{instance=~"$frontend",path!~"/health\|/metrics"}[$__rate_interval])))` | `timeseries` | HTTP P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99` |
| gRPC QPS per Instance | `sum by(instance, pod, path, code) (rate(greptime_servers_grpc_requests_elapsed_count{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | gRPC QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{code}}]` |
| gRPC P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, path, code) (rate(greptime_servers_grpc_requests_elapsed_bucket{instance=~"$frontend"}[$__rate_interval])))` | `timeseries` | gRPC P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99` |
| MySQL QPS per Instance | `sum by(pod, instance)(rate(greptime_servers_mysql_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | MySQL QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]` |
| MySQL P99 per Instance | `histogram_quantile(0.99, sum by(pod, instance, le) (rate(greptime_servers_mysql_query_elapsed_bucket{instance=~"$frontend"}[$__rate_interval])))` | `timeseries` | MySQL P99 per Instance. | `prometheus` | `s` | `[{{ instance }}]-[{{ pod }}]-p99` |
| PostgreSQL QPS per Instance | `sum by(pod, instance)(rate(greptime_servers_postgres_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | PostgreSQL QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]` |
| PostgreSQL P99 per Instance | `histogram_quantile(0.99, sum by(pod,instance,le) (rate(greptime_servers_postgres_query_elapsed_bucket{instance=~"$frontend"}[$__rate_interval])))` | `timeseries` | PostgreSQL P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-p99` |
# Frontend to Datanode
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Ingest Rows per Instance | `sum by(instance, pod)(rate(greptime_table_operator_ingest_rows{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | Ingestion rate by row as in each frontend | `prometheus` | `rowsps` | `[{{instance}}]-[{{pod}}]` |
| Region Call QPS per Instance | `sum by(instance, pod, request_type) (rate(greptime_grpc_region_request_count{instance=~"$frontend"}[$__rate_interval]))` | `timeseries` | Region Call QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{request_type}}]` |
| Region Call P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, request_type) (rate(greptime_grpc_region_request_bucket{instance=~"$frontend"}[$__rate_interval])))` | `timeseries` | Region Call P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{request_type}}]` |
# Mito Engine
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Request OPS per Instance | `sum by(instance, pod, type) (rate(greptime_mito_handle_request_elapsed_count{instance=~"$datanode"}[$__rate_interval]))` | `timeseries` | Request QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{type}}]` |
| Request P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, type) (rate(greptime_mito_handle_request_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))` | `timeseries` | Request P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{type}}]` |
| Write Buffer per Instance | `greptime_mito_write_buffer_bytes{instance=~"$datanode"}` | `timeseries` | Write Buffer per Instance. | `prometheus` | `decbytes` | `[{{instance}}]-[{{pod}}]` |
| Write Rows per Instance | `sum by (instance, pod) (rate(greptime_mito_write_rows_total{instance=~"$datanode"}[$__rate_interval]))` | `timeseries` | Ingestion size by row counts. | `prometheus` | `rowsps` | `[{{instance}}]-[{{pod}}]` |
| Flush OPS per Instance | `sum by(instance, pod, reason) (rate(greptime_mito_flush_requests_total{instance=~"$datanode"}[$__rate_interval]))` | `timeseries` | Flush QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{reason}}]` |
| Write Stall per Instance | `sum by(instance, pod) (greptime_mito_write_stall_total{instance=~"$datanode"})` | `timeseries` | Write Stall per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]` |
| Read Stage OPS per Instance | `sum by(instance, pod) (rate(greptime_mito_read_stage_elapsed_count{instance=~"$datanode", stage="total"}[$__rate_interval]))` | `timeseries` | Read Stage OPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]` |
| Read Stage P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_read_stage_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))` | `timeseries` | Read Stage P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]` |
| Write Stage P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_write_stage_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))` | `timeseries` | Write Stage P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]` |
| Compaction OPS per Instance | `sum by(instance, pod) (rate(greptime_mito_compaction_total_elapsed_count{instance=~"$datanode"}[$__rate_interval]))` | `timeseries` | Compaction OPS per Instance. | `prometheus` | `ops` | `[{{ instance }}]-[{{pod}}]` |
| Compaction P99 per Instance by Stage | `histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_compaction_stage_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))` | `timeseries` | Compaction latency by stage | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-p99` |
| Compaction P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le,stage) (rate(greptime_mito_compaction_total_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))` | `timeseries` | Compaction P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-compaction` |
| WAL write size | `histogram_quantile(0.95, sum by(le,instance, pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))`<br/>`histogram_quantile(0.99, sum by(le,instance,pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))`<br/>`sum by (instance, pod)(rate(raft_engine_write_size_sum[$__rate_interval]))` | `timeseries` | Write-ahead logs write size as bytes. This chart includes stats of p95 and p99 size by instance, total WAL write rate. | `prometheus` | `bytes` | `[{{instance}}]-[{{pod}}]-req-size-p95` |
| Cached Bytes per Instance | `greptime_mito_cache_bytes{instance=~"$datanode"}` | `timeseries` | Cached Bytes per Instance. | `prometheus` | `decbytes` | `[{{instance}}]-[{{pod}}]-[{{type}}]` |
| Inflight Compaction | `greptime_mito_inflight_compaction_count` | `timeseries` | Ongoing compaction task count | `prometheus` | `none` | `[{{instance}}]-[{{pod}}]` |
| WAL sync duration seconds | `histogram_quantile(0.99, sum by(le, type, node, instance, pod) (rate(raft_engine_sync_log_duration_seconds_bucket[$__rate_interval])))` | `timeseries` | Raft engine (local disk) log store sync latency, p99 | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-p99` |
| Log Store op duration seconds | `histogram_quantile(0.99, sum by(le,logstore,optype,instance, pod) (rate(greptime_logstore_op_elapsed_bucket[$__rate_interval])))` | `timeseries` | Write-ahead log operations latency at p99 | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{logstore}}]-[{{optype}}]-p99` |
| Inflight Flush | `greptime_mito_inflight_flush_count` | `timeseries` | Ongoing flush task count | `prometheus` | `none` | `[{{instance}}]-[{{pod}}]` |
# OpenDAL
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| QPS per Instance | `sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode"}[$__rate_interval]))` | `timeseries` | QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
| Read QPS per Instance | `sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode", operation="read"}[$__rate_interval]))` | `timeseries` | Read QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
| Read P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode",operation="read"}[$__rate_interval])))` | `timeseries` | Read P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-{{scheme}}` |
| Write QPS per Instance | `sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode", operation="write"}[$__rate_interval]))` | `timeseries` | Write QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-{{scheme}}` |
| Write P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode", operation="write"}[$__rate_interval])))` | `timeseries` | Write P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
| List QPS per Instance | `sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode", operation="list"}[$__rate_interval]))` | `timeseries` | List QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
| List P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode", operation="list"}[$__rate_interval])))` | `timeseries` | List P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
| Other Requests per Instance | `sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode",operation!~"read\|write\|list\|stat"}[$__rate_interval]))` | `timeseries` | Other Requests per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
| Other Request P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme, operation) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode", operation!~"read\|write\|list"}[$__rate_interval])))` | `timeseries` | Other Request P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
| Opendal traffic | `sum by(instance, pod, scheme, operation) (rate(opendal_operation_bytes_sum{instance=~"$datanode"}[$__rate_interval]))` | `timeseries` | Total traffic as in bytes by instance and operation | `prometheus` | `decbytes` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
| OpenDAL errors per Instance | `sum by(instance, pod, scheme, operation, error) (rate(opendal_operation_errors_total{instance=~"$datanode", error!="NotFound"}[$__rate_interval]))` | `timeseries` | OpenDAL error counts per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]-[{{error}}]` |
# Metasrv
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Region migration datanode | `greptime_meta_region_migration_stat{datanode_type="src"}`<br/>`greptime_meta_region_migration_stat{datanode_type="desc"}` | `state-timeline` | Counter of region migration by source and destination | `prometheus` | `none` | `from-datanode-{{datanode_id}}` |
| Region migration error | `greptime_meta_region_migration_error` | `timeseries` | Counter of region migration error | `prometheus` | `none` | `__auto` |
| Datanode load | `greptime_datanode_load` | `timeseries` | Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads. | `prometheus` | `none` | `__auto` |
# Flownode
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Flow Ingest / Output Rate | `sum by(instance, pod, direction) (rate(greptime_flow_processed_rows[$__rate_interval]))` | `timeseries` | Flow Ingest / Output Rate. | `prometheus` | -- | `[{{pod}}]-[{{instance}}]-[{{direction}}]` |
| Flow Ingest Latency | `histogram_quantile(0.95, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))`<br/>`histogram_quantile(0.99, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))` | `timeseries` | Flow Ingest Latency. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-p95` |
| Flow Operation Latency | `histogram_quantile(0.95, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))`<br/>`histogram_quantile(0.99, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))` | `timeseries` | Flow Operation Latency. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-[{{type}}]-p95` |
| Flow Buffer Size per Instance | `greptime_flow_input_buf_size` | `timeseries` | Flow Buffer Size per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}]` |
| Flow Processing Error per Instance | `sum by(instance,pod,code) (rate(greptime_flow_errors[$__rate_interval]))` | `timeseries` | Flow Processing Error per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-[{{code}}]` |

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groups:
- title: Overview
panels:
- title: Uptime
type: stat
description: The start time of GreptimeDB.
unit: s
queries:
- expr: time() - process_start_time_seconds
datasource:
type: prometheus
uid: ${metrics}
legendFormat: __auto
- title: Version
type: stat
description: GreptimeDB version.
queries:
- expr: SELECT pkg_version FROM information_schema.build_info
datasource:
type: mysql
uid: ${information_schema}
- title: Total Ingestion Rate
type: stat
description: Total ingestion rate.
unit: rowsps
queries:
- expr: sum(rate(greptime_table_operator_ingest_rows[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: __auto
- title: Total Storage Size
type: stat
description: Total number of data file size.
unit: decbytes
queries:
- expr: select SUM(disk_size) from information_schema.region_statistics;
datasource:
type: mysql
uid: ${information_schema}
- title: Total Rows
type: stat
description: Total number of data rows in the cluster. Calculated by sum of rows from each region.
unit: sishort
queries:
- expr: select SUM(region_rows) from information_schema.region_statistics;
datasource:
type: mysql
uid: ${information_schema}
- title: Deployment
type: stat
description: The deployment topology of GreptimeDB.
queries:
- expr: SELECT count(*) as datanode FROM information_schema.cluster_info WHERE peer_type = 'DATANODE';
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT count(*) as frontend FROM information_schema.cluster_info WHERE peer_type = 'FRONTEND';
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT count(*) as metasrv FROM information_schema.cluster_info WHERE peer_type = 'METASRV';
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT count(*) as flownode FROM information_schema.cluster_info WHERE peer_type = 'FLOWNODE';
datasource:
type: mysql
uid: ${information_schema}
- title: Database Resources
type: stat
description: The number of the key resources in GreptimeDB.
queries:
- expr: SELECT COUNT(*) as databases FROM information_schema.schemata WHERE schema_name NOT IN ('greptime_private', 'information_schema')
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT COUNT(*) as tables FROM information_schema.tables WHERE table_schema != 'information_schema'
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT COUNT(region_id) as regions FROM information_schema.region_peers
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT COUNT(*) as flows FROM information_schema.flows
datasource:
type: mysql
uid: ${information_schema}
- title: Data Size
type: stat
description: The data size of wal/index/manifest in the GreptimeDB.
unit: decbytes
queries:
- expr: SELECT SUM(memtable_size) * 0.42825 as WAL FROM information_schema.region_statistics;
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT SUM(index_size) as index FROM information_schema.region_statistics;
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT SUM(manifest_size) as manifest FROM information_schema.region_statistics;
datasource:
type: mysql
uid: ${information_schema}
- title: Ingestion
panels:
- title: Total Ingestion Rate
type: timeseries
description: |
Total ingestion rate.
Here we listed 3 primary protocols:
- Prometheus remote write
- Greptime's gRPC API (when using our ingest SDK)
- Log ingestion http API
unit: rowsps
queries:
- expr: sum(rate(greptime_table_operator_ingest_rows{instance=~"$frontend"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: ingestion
- title: Ingestion Rate by Type
type: timeseries
description: |
Total ingestion rate.
Here we listed 3 primary protocols:
- Prometheus remote write
- Greptime's gRPC API (when using our ingest SDK)
- Log ingestion http API
unit: rowsps
queries:
- expr: sum(rate(greptime_servers_http_logs_ingestion_counter[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: http-logs
- expr: sum(rate(greptime_servers_prometheus_remote_write_samples[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: prometheus-remote-write
- title: Queries
panels:
- title: Total Query Rate
type: timeseries
description: |-
Total rate of query API calls by protocol. This metric is collected from frontends.
Here we listed 3 main protocols:
- MySQL
- Postgres
- Prometheus API
Note that there are some other minor query APIs like /sql are not included
unit: reqps
queries:
- expr: sum (rate(greptime_servers_mysql_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: mysql
- expr: sum (rate(greptime_servers_postgres_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: pg
- expr: sum (rate(greptime_servers_http_promql_elapsed_counte{instance=~"$frontend"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: promql
- title: Resources
panels:
- title: Datanode Memory per Instance
type: timeseries
description: Current memory usage by instance
unit: decbytes
queries:
- expr: sum(process_resident_memory_bytes{instance=~"$datanode"}) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{ pod }}]'
- title: Datanode CPU Usage per Instance
type: timeseries
description: Current cpu usage by instance
unit: none
queries:
- expr: sum(rate(process_cpu_seconds_total{instance=~"$datanode"}[$__rate_interval]) * 1000) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]'
- title: Frontend Memory per Instance
type: timeseries
description: Current memory usage by instance
unit: decbytes
queries:
- expr: sum(process_resident_memory_bytes{instance=~"$frontend"}) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]'
- title: Frontend CPU Usage per Instance
type: timeseries
description: Current cpu usage by instance
unit: none
queries:
- expr: sum(rate(process_cpu_seconds_total{instance=~"$frontend"}[$__rate_interval]) * 1000) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]-cpu'
- title: Metasrv Memory per Instance
type: timeseries
description: Current memory usage by instance
unit: decbytes
queries:
- expr: sum(process_resident_memory_bytes{instance=~"$metasrv"}) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]-resident'
- title: Metasrv CPU Usage per Instance
type: timeseries
description: Current cpu usage by instance
unit: none
queries:
- expr: sum(rate(process_cpu_seconds_total{instance=~"$metasrv"}[$__rate_interval]) * 1000) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]'
- title: Flownode Memory per Instance
type: timeseries
description: Current memory usage by instance
unit: decbytes
queries:
- expr: sum(process_resident_memory_bytes{instance=~"$flownode"}) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]'
- title: Flownode CPU Usage per Instance
type: timeseries
description: Current cpu usage by instance
unit: none
queries:
- expr: sum(rate(process_cpu_seconds_total{instance=~"$flownode"}[$__rate_interval]) * 1000) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]'
- title: Frontend Requests
panels:
- title: HTTP QPS per Instance
type: timeseries
description: HTTP QPS per Instance.
unit: reqps
queries:
- expr: sum by(instance, pod, path, method, code) (rate(greptime_servers_http_requests_elapsed_count{instance=~"$frontend",path!~"/health|/metrics"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]'
- title: HTTP P99 per Instance
type: timeseries
description: HTTP P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, path, method, code) (rate(greptime_servers_http_requests_elapsed_bucket{instance=~"$frontend",path!~"/health|/metrics"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99'
- title: gRPC QPS per Instance
type: timeseries
description: gRPC QPS per Instance.
unit: reqps
queries:
- expr: sum by(instance, pod, path, code) (rate(greptime_servers_grpc_requests_elapsed_count{instance=~"$frontend"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{code}}]'
- title: gRPC P99 per Instance
type: timeseries
description: gRPC P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, path, code) (rate(greptime_servers_grpc_requests_elapsed_bucket{instance=~"$frontend"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99'
- title: MySQL QPS per Instance
type: timeseries
description: MySQL QPS per Instance.
unit: reqps
queries:
- expr: sum by(pod, instance)(rate(greptime_servers_mysql_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: MySQL P99 per Instance
type: timeseries
description: MySQL P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(pod, instance, le) (rate(greptime_servers_mysql_query_elapsed_bucket{instance=~"$frontend"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]-p99'
- title: PostgreSQL QPS per Instance
type: timeseries
description: PostgreSQL QPS per Instance.
unit: reqps
queries:
- expr: sum by(pod, instance)(rate(greptime_servers_postgres_query_elapsed_count{instance=~"$frontend"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: PostgreSQL P99 per Instance
type: timeseries
description: PostgreSQL P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(pod,instance,le) (rate(greptime_servers_postgres_query_elapsed_bucket{instance=~"$frontend"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-p99'
- title: Frontend to Datanode
panels:
- title: Ingest Rows per Instance
type: timeseries
description: Ingestion rate by row as in each frontend
unit: rowsps
queries:
- expr: sum by(instance, pod)(rate(greptime_table_operator_ingest_rows{instance=~"$frontend"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: Region Call QPS per Instance
type: timeseries
description: Region Call QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, request_type) (rate(greptime_grpc_region_request_count{instance=~"$frontend"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{request_type}}]'
- title: Region Call P99 per Instance
type: timeseries
description: Region Call P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, request_type) (rate(greptime_grpc_region_request_bucket{instance=~"$frontend"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{request_type}}]'
- title: Mito Engine
panels:
- title: Request OPS per Instance
type: timeseries
description: Request QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, type) (rate(greptime_mito_handle_request_elapsed_count{instance=~"$datanode"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]'
- title: Request P99 per Instance
type: timeseries
description: Request P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, type) (rate(greptime_mito_handle_request_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]'
- title: Write Buffer per Instance
type: timeseries
description: Write Buffer per Instance.
unit: decbytes
queries:
- expr: greptime_mito_write_buffer_bytes{instance=~"$datanode"}
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: Write Rows per Instance
type: timeseries
description: Ingestion size by row counts.
unit: rowsps
queries:
- expr: sum by (instance, pod) (rate(greptime_mito_write_rows_total{instance=~"$datanode"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: Flush OPS per Instance
type: timeseries
description: Flush QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, reason) (rate(greptime_mito_flush_requests_total{instance=~"$datanode"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{reason}}]'
- title: Write Stall per Instance
type: timeseries
description: Write Stall per Instance.
queries:
- expr: sum by(instance, pod) (greptime_mito_write_stall_total{instance=~"$datanode"})
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: Read Stage OPS per Instance
type: timeseries
description: Read Stage OPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod) (rate(greptime_mito_read_stage_elapsed_count{instance=~"$datanode", stage="total"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: Read Stage P99 per Instance
type: timeseries
description: Read Stage P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_read_stage_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]'
- title: Write Stage P99 per Instance
type: timeseries
description: Write Stage P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_write_stage_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]'
- title: Compaction OPS per Instance
type: timeseries
description: Compaction OPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod) (rate(greptime_mito_compaction_total_elapsed_count{instance=~"$datanode"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{pod}}]'
- title: Compaction P99 per Instance by Stage
type: timeseries
description: Compaction latency by stage
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_compaction_stage_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-p99'
- title: Compaction P99 per Instance
type: timeseries
description: Compaction P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le,stage) (rate(greptime_mito_compaction_total_elapsed_bucket{instance=~"$datanode"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-compaction'
- title: WAL write size
type: timeseries
description: Write-ahead logs write size as bytes. This chart includes stats of p95 and p99 size by instance, total WAL write rate.
unit: bytes
queries:
- expr: histogram_quantile(0.95, sum by(le,instance, pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-req-size-p95'
- expr: histogram_quantile(0.99, sum by(le,instance,pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-req-size-p99'
- expr: sum by (instance, pod)(rate(raft_engine_write_size_sum[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-throughput'
- title: Cached Bytes per Instance
type: timeseries
description: Cached Bytes per Instance.
unit: decbytes
queries:
- expr: greptime_mito_cache_bytes{instance=~"$datanode"}
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]'
- title: Inflight Compaction
type: timeseries
description: Ongoing compaction task count
unit: none
queries:
- expr: greptime_mito_inflight_compaction_count
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: WAL sync duration seconds
type: timeseries
description: Raft engine (local disk) log store sync latency, p99
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(le, type, node, instance, pod) (rate(raft_engine_sync_log_duration_seconds_bucket[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-p99'
- title: Log Store op duration seconds
type: timeseries
description: Write-ahead log operations latency at p99
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(le,logstore,optype,instance, pod) (rate(greptime_logstore_op_elapsed_bucket[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{logstore}}]-[{{optype}}]-p99'
- title: Inflight Flush
type: timeseries
description: Ongoing flush task count
unit: none
queries:
- expr: greptime_mito_inflight_flush_count
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: OpenDAL
panels:
- title: QPS per Instance
type: timeseries
description: QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
- title: Read QPS per Instance
type: timeseries
description: Read QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode", operation="read"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
- title: Read P99 per Instance
type: timeseries
description: Read P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode",operation="read"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-{{scheme}}'
- title: Write QPS per Instance
type: timeseries
description: Write QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode", operation="write"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-{{scheme}}'
- title: Write P99 per Instance
type: timeseries
description: Write P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode", operation="write"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
- title: List QPS per Instance
type: timeseries
description: List QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode", operation="list"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
- title: List P99 per Instance
type: timeseries
description: List P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode", operation="list"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
- title: Other Requests per Instance
type: timeseries
description: Other Requests per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{instance=~"$datanode",operation!~"read|write|list|stat"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
- title: Other Request P99 per Instance
type: timeseries
description: Other Request P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme, operation) (rate(opendal_operation_duration_seconds_bucket{instance=~"$datanode", operation!~"read|write|list"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
- title: Opendal traffic
type: timeseries
description: Total traffic as in bytes by instance and operation
unit: decbytes
queries:
- expr: sum by(instance, pod, scheme, operation) (rate(opendal_operation_bytes_sum{instance=~"$datanode"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
- title: OpenDAL errors per Instance
type: timeseries
description: OpenDAL error counts per Instance.
queries:
- expr: sum by(instance, pod, scheme, operation, error) (rate(opendal_operation_errors_total{instance=~"$datanode", error!="NotFound"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]-[{{error}}]'
- title: Metasrv
panels:
- title: Region migration datanode
type: state-timeline
description: Counter of region migration by source and destination
unit: none
queries:
- expr: greptime_meta_region_migration_stat{datanode_type="src"}
datasource:
type: prometheus
uid: ${metrics}
legendFormat: from-datanode-{{datanode_id}}
- expr: greptime_meta_region_migration_stat{datanode_type="desc"}
datasource:
type: prometheus
uid: ${metrics}
legendFormat: to-datanode-{{datanode_id}}
- title: Region migration error
type: timeseries
description: Counter of region migration error
unit: none
queries:
- expr: greptime_meta_region_migration_error
datasource:
type: prometheus
uid: ${metrics}
legendFormat: __auto
- title: Datanode load
type: timeseries
description: Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads.
unit: none
queries:
- expr: greptime_datanode_load
datasource:
type: prometheus
uid: ${metrics}
legendFormat: __auto
- title: Flownode
panels:
- title: Flow Ingest / Output Rate
type: timeseries
description: Flow Ingest / Output Rate.
queries:
- expr: sum by(instance, pod, direction) (rate(greptime_flow_processed_rows[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{pod}}]-[{{instance}}]-[{{direction}}]'
- title: Flow Ingest Latency
type: timeseries
description: Flow Ingest Latency.
queries:
- expr: histogram_quantile(0.95, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-p95'
- expr: histogram_quantile(0.99, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-p99'
- title: Flow Operation Latency
type: timeseries
description: Flow Operation Latency.
queries:
- expr: histogram_quantile(0.95, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]-p95'
- expr: histogram_quantile(0.99, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]-p99'
- title: Flow Buffer Size per Instance
type: timeseries
description: Flow Buffer Size per Instance.
queries:
- expr: greptime_flow_input_buf_size
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}]'
- title: Flow Processing Error per Instance
type: timeseries
description: Flow Processing Error per Instance.
queries:
- expr: sum by(instance,pod,code) (rate(greptime_flow_errors[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{code}}]'

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# Overview
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Uptime | `time() - process_start_time_seconds` | `stat` | The start time of GreptimeDB. | `prometheus` | `s` | `__auto` |
| Version | `SELECT pkg_version FROM information_schema.build_info` | `stat` | GreptimeDB version. | `mysql` | -- | -- |
| Total Ingestion Rate | `sum(rate(greptime_table_operator_ingest_rows[$__rate_interval]))` | `stat` | Total ingestion rate. | `prometheus` | `rowsps` | `__auto` |
| Total Storage Size | `select SUM(disk_size) from information_schema.region_statistics;` | `stat` | Total number of data file size. | `mysql` | `decbytes` | -- |
| Total Rows | `select SUM(region_rows) from information_schema.region_statistics;` | `stat` | Total number of data rows in the cluster. Calculated by sum of rows from each region. | `mysql` | `sishort` | -- |
| Deployment | `SELECT count(*) as datanode FROM information_schema.cluster_info WHERE peer_type = 'DATANODE';`<br/>`SELECT count(*) as frontend FROM information_schema.cluster_info WHERE peer_type = 'FRONTEND';`<br/>`SELECT count(*) as metasrv FROM information_schema.cluster_info WHERE peer_type = 'METASRV';`<br/>`SELECT count(*) as flownode FROM information_schema.cluster_info WHERE peer_type = 'FLOWNODE';` | `stat` | The deployment topology of GreptimeDB. | `mysql` | -- | -- |
| Database Resources | `SELECT COUNT(*) as databases FROM information_schema.schemata WHERE schema_name NOT IN ('greptime_private', 'information_schema')`<br/>`SELECT COUNT(*) as tables FROM information_schema.tables WHERE table_schema != 'information_schema'`<br/>`SELECT COUNT(region_id) as regions FROM information_schema.region_peers`<br/>`SELECT COUNT(*) as flows FROM information_schema.flows` | `stat` | The number of the key resources in GreptimeDB. | `mysql` | -- | -- |
| Data Size | `SELECT SUM(memtable_size) * 0.42825 as WAL FROM information_schema.region_statistics;`<br/>`SELECT SUM(index_size) as index FROM information_schema.region_statistics;`<br/>`SELECT SUM(manifest_size) as manifest FROM information_schema.region_statistics;` | `stat` | The data size of wal/index/manifest in the GreptimeDB. | `mysql` | `decbytes` | -- |
# Ingestion
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Total Ingestion Rate | `sum(rate(greptime_table_operator_ingest_rows{}[$__rate_interval]))` | `timeseries` | Total ingestion rate.<br/><br/>Here we listed 3 primary protocols:<br/><br/>- Prometheus remote write<br/>- Greptime's gRPC API (when using our ingest SDK)<br/>- Log ingestion http API<br/> | `prometheus` | `rowsps` | `ingestion` |
| Ingestion Rate by Type | `sum(rate(greptime_servers_http_logs_ingestion_counter[$__rate_interval]))`<br/>`sum(rate(greptime_servers_prometheus_remote_write_samples[$__rate_interval]))` | `timeseries` | Total ingestion rate.<br/><br/>Here we listed 3 primary protocols:<br/><br/>- Prometheus remote write<br/>- Greptime's gRPC API (when using our ingest SDK)<br/>- Log ingestion http API<br/> | `prometheus` | `rowsps` | `http-logs` |
# Queries
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Total Query Rate | `sum (rate(greptime_servers_mysql_query_elapsed_count{}[$__rate_interval]))`<br/>`sum (rate(greptime_servers_postgres_query_elapsed_count{}[$__rate_interval]))`<br/>`sum (rate(greptime_servers_http_promql_elapsed_counte{}[$__rate_interval]))` | `timeseries` | Total rate of query API calls by protocol. This metric is collected from frontends.<br/><br/>Here we listed 3 main protocols:<br/>- MySQL<br/>- Postgres<br/>- Prometheus API<br/><br/>Note that there are some other minor query APIs like /sql are not included | `prometheus` | `reqps` | `mysql` |
# Resources
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Datanode Memory per Instance | `sum(process_resident_memory_bytes{}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{instance}}]-[{{ pod }}]` |
| Datanode CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]` |
| Frontend Memory per Instance | `sum(process_resident_memory_bytes{}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{ instance }}]-[{{ pod }}]` |
| Frontend CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]-cpu` |
| Metasrv Memory per Instance | `sum(process_resident_memory_bytes{}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{ instance }}]-[{{ pod }}]-resident` |
| Metasrv CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]` |
| Flownode Memory per Instance | `sum(process_resident_memory_bytes{}) by (instance, pod)` | `timeseries` | Current memory usage by instance | `prometheus` | `decbytes` | `[{{ instance }}]-[{{ pod }}]` |
| Flownode CPU Usage per Instance | `sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)` | `timeseries` | Current cpu usage by instance | `prometheus` | `none` | `[{{ instance }}]-[{{ pod }}]` |
# Frontend Requests
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| HTTP QPS per Instance | `sum by(instance, pod, path, method, code) (rate(greptime_servers_http_requests_elapsed_count{path!~"/health\|/metrics"}[$__rate_interval]))` | `timeseries` | HTTP QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]` |
| HTTP P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, path, method, code) (rate(greptime_servers_http_requests_elapsed_bucket{path!~"/health\|/metrics"}[$__rate_interval])))` | `timeseries` | HTTP P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99` |
| gRPC QPS per Instance | `sum by(instance, pod, path, code) (rate(greptime_servers_grpc_requests_elapsed_count{}[$__rate_interval]))` | `timeseries` | gRPC QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{code}}]` |
| gRPC P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, path, code) (rate(greptime_servers_grpc_requests_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | gRPC P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99` |
| MySQL QPS per Instance | `sum by(pod, instance)(rate(greptime_servers_mysql_query_elapsed_count{}[$__rate_interval]))` | `timeseries` | MySQL QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]` |
| MySQL P99 per Instance | `histogram_quantile(0.99, sum by(pod, instance, le) (rate(greptime_servers_mysql_query_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | MySQL P99 per Instance. | `prometheus` | `s` | `[{{ instance }}]-[{{ pod }}]-p99` |
| PostgreSQL QPS per Instance | `sum by(pod, instance)(rate(greptime_servers_postgres_query_elapsed_count{}[$__rate_interval]))` | `timeseries` | PostgreSQL QPS per Instance. | `prometheus` | `reqps` | `[{{instance}}]-[{{pod}}]` |
| PostgreSQL P99 per Instance | `histogram_quantile(0.99, sum by(pod,instance,le) (rate(greptime_servers_postgres_query_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | PostgreSQL P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-p99` |
# Frontend to Datanode
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Ingest Rows per Instance | `sum by(instance, pod)(rate(greptime_table_operator_ingest_rows{}[$__rate_interval]))` | `timeseries` | Ingestion rate by row as in each frontend | `prometheus` | `rowsps` | `[{{instance}}]-[{{pod}}]` |
| Region Call QPS per Instance | `sum by(instance, pod, request_type) (rate(greptime_grpc_region_request_count{}[$__rate_interval]))` | `timeseries` | Region Call QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{request_type}}]` |
| Region Call P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, request_type) (rate(greptime_grpc_region_request_bucket{}[$__rate_interval])))` | `timeseries` | Region Call P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{request_type}}]` |
# Mito Engine
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Request OPS per Instance | `sum by(instance, pod, type) (rate(greptime_mito_handle_request_elapsed_count{}[$__rate_interval]))` | `timeseries` | Request QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{type}}]` |
| Request P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, type) (rate(greptime_mito_handle_request_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | Request P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{type}}]` |
| Write Buffer per Instance | `greptime_mito_write_buffer_bytes{}` | `timeseries` | Write Buffer per Instance. | `prometheus` | `decbytes` | `[{{instance}}]-[{{pod}}]` |
| Write Rows per Instance | `sum by (instance, pod) (rate(greptime_mito_write_rows_total{}[$__rate_interval]))` | `timeseries` | Ingestion size by row counts. | `prometheus` | `rowsps` | `[{{instance}}]-[{{pod}}]` |
| Flush OPS per Instance | `sum by(instance, pod, reason) (rate(greptime_mito_flush_requests_total{}[$__rate_interval]))` | `timeseries` | Flush QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{reason}}]` |
| Write Stall per Instance | `sum by(instance, pod) (greptime_mito_write_stall_total{})` | `timeseries` | Write Stall per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]` |
| Read Stage OPS per Instance | `sum by(instance, pod) (rate(greptime_mito_read_stage_elapsed_count{ stage="total"}[$__rate_interval]))` | `timeseries` | Read Stage OPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]` |
| Read Stage P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_read_stage_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | Read Stage P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]` |
| Write Stage P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_write_stage_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | Write Stage P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]` |
| Compaction OPS per Instance | `sum by(instance, pod) (rate(greptime_mito_compaction_total_elapsed_count{}[$__rate_interval]))` | `timeseries` | Compaction OPS per Instance. | `prometheus` | `ops` | `[{{ instance }}]-[{{pod}}]` |
| Compaction P99 per Instance by Stage | `histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_compaction_stage_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | Compaction latency by stage | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-p99` |
| Compaction P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le,stage) (rate(greptime_mito_compaction_total_elapsed_bucket{}[$__rate_interval])))` | `timeseries` | Compaction P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{stage}}]-compaction` |
| WAL write size | `histogram_quantile(0.95, sum by(le,instance, pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))`<br/>`histogram_quantile(0.99, sum by(le,instance,pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))`<br/>`sum by (instance, pod)(rate(raft_engine_write_size_sum[$__rate_interval]))` | `timeseries` | Write-ahead logs write size as bytes. This chart includes stats of p95 and p99 size by instance, total WAL write rate. | `prometheus` | `bytes` | `[{{instance}}]-[{{pod}}]-req-size-p95` |
| Cached Bytes per Instance | `greptime_mito_cache_bytes{}` | `timeseries` | Cached Bytes per Instance. | `prometheus` | `decbytes` | `[{{instance}}]-[{{pod}}]-[{{type}}]` |
| Inflight Compaction | `greptime_mito_inflight_compaction_count` | `timeseries` | Ongoing compaction task count | `prometheus` | `none` | `[{{instance}}]-[{{pod}}]` |
| WAL sync duration seconds | `histogram_quantile(0.99, sum by(le, type, node, instance, pod) (rate(raft_engine_sync_log_duration_seconds_bucket[$__rate_interval])))` | `timeseries` | Raft engine (local disk) log store sync latency, p99 | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-p99` |
| Log Store op duration seconds | `histogram_quantile(0.99, sum by(le,logstore,optype,instance, pod) (rate(greptime_logstore_op_elapsed_bucket[$__rate_interval])))` | `timeseries` | Write-ahead log operations latency at p99 | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{logstore}}]-[{{optype}}]-p99` |
| Inflight Flush | `greptime_mito_inflight_flush_count` | `timeseries` | Ongoing flush task count | `prometheus` | `none` | `[{{instance}}]-[{{pod}}]` |
# OpenDAL
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| QPS per Instance | `sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{}[$__rate_interval]))` | `timeseries` | QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
| Read QPS per Instance | `sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{ operation="read"}[$__rate_interval]))` | `timeseries` | Read QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
| Read P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{operation="read"}[$__rate_interval])))` | `timeseries` | Read P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-{{scheme}}` |
| Write QPS per Instance | `sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{ operation="write"}[$__rate_interval]))` | `timeseries` | Write QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-{{scheme}}` |
| Write P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{ operation="write"}[$__rate_interval])))` | `timeseries` | Write P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
| List QPS per Instance | `sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{ operation="list"}[$__rate_interval]))` | `timeseries` | List QPS per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
| List P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{ operation="list"}[$__rate_interval])))` | `timeseries` | List P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]` |
| Other Requests per Instance | `sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{operation!~"read\|write\|list\|stat"}[$__rate_interval]))` | `timeseries` | Other Requests per Instance. | `prometheus` | `ops` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
| Other Request P99 per Instance | `histogram_quantile(0.99, sum by(instance, pod, le, scheme, operation) (rate(opendal_operation_duration_seconds_bucket{ operation!~"read\|write\|list"}[$__rate_interval])))` | `timeseries` | Other Request P99 per Instance. | `prometheus` | `s` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
| Opendal traffic | `sum by(instance, pod, scheme, operation) (rate(opendal_operation_bytes_sum{}[$__rate_interval]))` | `timeseries` | Total traffic as in bytes by instance and operation | `prometheus` | `decbytes` | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]` |
| OpenDAL errors per Instance | `sum by(instance, pod, scheme, operation, error) (rate(opendal_operation_errors_total{ error!="NotFound"}[$__rate_interval]))` | `timeseries` | OpenDAL error counts per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]-[{{error}}]` |
# Metasrv
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Region migration datanode | `greptime_meta_region_migration_stat{datanode_type="src"}`<br/>`greptime_meta_region_migration_stat{datanode_type="desc"}` | `state-timeline` | Counter of region migration by source and destination | `prometheus` | `none` | `from-datanode-{{datanode_id}}` |
| Region migration error | `greptime_meta_region_migration_error` | `timeseries` | Counter of region migration error | `prometheus` | `none` | `__auto` |
| Datanode load | `greptime_datanode_load` | `timeseries` | Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads. | `prometheus` | `none` | `__auto` |
# Flownode
| Title | Query | Type | Description | Datasource | Unit | Legend Format |
| --- | --- | --- | --- | --- | --- | --- |
| Flow Ingest / Output Rate | `sum by(instance, pod, direction) (rate(greptime_flow_processed_rows[$__rate_interval]))` | `timeseries` | Flow Ingest / Output Rate. | `prometheus` | -- | `[{{pod}}]-[{{instance}}]-[{{direction}}]` |
| Flow Ingest Latency | `histogram_quantile(0.95, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))`<br/>`histogram_quantile(0.99, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))` | `timeseries` | Flow Ingest Latency. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-p95` |
| Flow Operation Latency | `histogram_quantile(0.95, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))`<br/>`histogram_quantile(0.99, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))` | `timeseries` | Flow Operation Latency. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-[{{type}}]-p95` |
| Flow Buffer Size per Instance | `greptime_flow_input_buf_size` | `timeseries` | Flow Buffer Size per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}]` |
| Flow Processing Error per Instance | `sum by(instance,pod,code) (rate(greptime_flow_errors[$__rate_interval]))` | `timeseries` | Flow Processing Error per Instance. | `prometheus` | -- | `[{{instance}}]-[{{pod}}]-[{{code}}]` |

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groups:
- title: Overview
panels:
- title: Uptime
type: stat
description: The start time of GreptimeDB.
unit: s
queries:
- expr: time() - process_start_time_seconds
datasource:
type: prometheus
uid: ${metrics}
legendFormat: __auto
- title: Version
type: stat
description: GreptimeDB version.
queries:
- expr: SELECT pkg_version FROM information_schema.build_info
datasource:
type: mysql
uid: ${information_schema}
- title: Total Ingestion Rate
type: stat
description: Total ingestion rate.
unit: rowsps
queries:
- expr: sum(rate(greptime_table_operator_ingest_rows[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: __auto
- title: Total Storage Size
type: stat
description: Total number of data file size.
unit: decbytes
queries:
- expr: select SUM(disk_size) from information_schema.region_statistics;
datasource:
type: mysql
uid: ${information_schema}
- title: Total Rows
type: stat
description: Total number of data rows in the cluster. Calculated by sum of rows from each region.
unit: sishort
queries:
- expr: select SUM(region_rows) from information_schema.region_statistics;
datasource:
type: mysql
uid: ${information_schema}
- title: Deployment
type: stat
description: The deployment topology of GreptimeDB.
queries:
- expr: SELECT count(*) as datanode FROM information_schema.cluster_info WHERE peer_type = 'DATANODE';
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT count(*) as frontend FROM information_schema.cluster_info WHERE peer_type = 'FRONTEND';
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT count(*) as metasrv FROM information_schema.cluster_info WHERE peer_type = 'METASRV';
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT count(*) as flownode FROM information_schema.cluster_info WHERE peer_type = 'FLOWNODE';
datasource:
type: mysql
uid: ${information_schema}
- title: Database Resources
type: stat
description: The number of the key resources in GreptimeDB.
queries:
- expr: SELECT COUNT(*) as databases FROM information_schema.schemata WHERE schema_name NOT IN ('greptime_private', 'information_schema')
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT COUNT(*) as tables FROM information_schema.tables WHERE table_schema != 'information_schema'
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT COUNT(region_id) as regions FROM information_schema.region_peers
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT COUNT(*) as flows FROM information_schema.flows
datasource:
type: mysql
uid: ${information_schema}
- title: Data Size
type: stat
description: The data size of wal/index/manifest in the GreptimeDB.
unit: decbytes
queries:
- expr: SELECT SUM(memtable_size) * 0.42825 as WAL FROM information_schema.region_statistics;
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT SUM(index_size) as index FROM information_schema.region_statistics;
datasource:
type: mysql
uid: ${information_schema}
- expr: SELECT SUM(manifest_size) as manifest FROM information_schema.region_statistics;
datasource:
type: mysql
uid: ${information_schema}
- title: Ingestion
panels:
- title: Total Ingestion Rate
type: timeseries
description: |
Total ingestion rate.
Here we listed 3 primary protocols:
- Prometheus remote write
- Greptime's gRPC API (when using our ingest SDK)
- Log ingestion http API
unit: rowsps
queries:
- expr: sum(rate(greptime_table_operator_ingest_rows{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: ingestion
- title: Ingestion Rate by Type
type: timeseries
description: |
Total ingestion rate.
Here we listed 3 primary protocols:
- Prometheus remote write
- Greptime's gRPC API (when using our ingest SDK)
- Log ingestion http API
unit: rowsps
queries:
- expr: sum(rate(greptime_servers_http_logs_ingestion_counter[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: http-logs
- expr: sum(rate(greptime_servers_prometheus_remote_write_samples[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: prometheus-remote-write
- title: Queries
panels:
- title: Total Query Rate
type: timeseries
description: |-
Total rate of query API calls by protocol. This metric is collected from frontends.
Here we listed 3 main protocols:
- MySQL
- Postgres
- Prometheus API
Note that there are some other minor query APIs like /sql are not included
unit: reqps
queries:
- expr: sum (rate(greptime_servers_mysql_query_elapsed_count{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: mysql
- expr: sum (rate(greptime_servers_postgres_query_elapsed_count{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: pg
- expr: sum (rate(greptime_servers_http_promql_elapsed_counte{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: promql
- title: Resources
panels:
- title: Datanode Memory per Instance
type: timeseries
description: Current memory usage by instance
unit: decbytes
queries:
- expr: sum(process_resident_memory_bytes{}) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{ pod }}]'
- title: Datanode CPU Usage per Instance
type: timeseries
description: Current cpu usage by instance
unit: none
queries:
- expr: sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]'
- title: Frontend Memory per Instance
type: timeseries
description: Current memory usage by instance
unit: decbytes
queries:
- expr: sum(process_resident_memory_bytes{}) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]'
- title: Frontend CPU Usage per Instance
type: timeseries
description: Current cpu usage by instance
unit: none
queries:
- expr: sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]-cpu'
- title: Metasrv Memory per Instance
type: timeseries
description: Current memory usage by instance
unit: decbytes
queries:
- expr: sum(process_resident_memory_bytes{}) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]-resident'
- title: Metasrv CPU Usage per Instance
type: timeseries
description: Current cpu usage by instance
unit: none
queries:
- expr: sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]'
- title: Flownode Memory per Instance
type: timeseries
description: Current memory usage by instance
unit: decbytes
queries:
- expr: sum(process_resident_memory_bytes{}) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]'
- title: Flownode CPU Usage per Instance
type: timeseries
description: Current cpu usage by instance
unit: none
queries:
- expr: sum(rate(process_cpu_seconds_total{}[$__rate_interval]) * 1000) by (instance, pod)
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]'
- title: Frontend Requests
panels:
- title: HTTP QPS per Instance
type: timeseries
description: HTTP QPS per Instance.
unit: reqps
queries:
- expr: sum by(instance, pod, path, method, code) (rate(greptime_servers_http_requests_elapsed_count{path!~"/health|/metrics"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]'
- title: HTTP P99 per Instance
type: timeseries
description: HTTP P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, path, method, code) (rate(greptime_servers_http_requests_elapsed_bucket{path!~"/health|/metrics"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99'
- title: gRPC QPS per Instance
type: timeseries
description: gRPC QPS per Instance.
unit: reqps
queries:
- expr: sum by(instance, pod, path, code) (rate(greptime_servers_grpc_requests_elapsed_count{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{code}}]'
- title: gRPC P99 per Instance
type: timeseries
description: gRPC P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, path, code) (rate(greptime_servers_grpc_requests_elapsed_bucket{}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{path}}]-[{{method}}]-[{{code}}]-p99'
- title: MySQL QPS per Instance
type: timeseries
description: MySQL QPS per Instance.
unit: reqps
queries:
- expr: sum by(pod, instance)(rate(greptime_servers_mysql_query_elapsed_count{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: MySQL P99 per Instance
type: timeseries
description: MySQL P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(pod, instance, le) (rate(greptime_servers_mysql_query_elapsed_bucket{}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{ pod }}]-p99'
- title: PostgreSQL QPS per Instance
type: timeseries
description: PostgreSQL QPS per Instance.
unit: reqps
queries:
- expr: sum by(pod, instance)(rate(greptime_servers_postgres_query_elapsed_count{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: PostgreSQL P99 per Instance
type: timeseries
description: PostgreSQL P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(pod,instance,le) (rate(greptime_servers_postgres_query_elapsed_bucket{}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-p99'
- title: Frontend to Datanode
panels:
- title: Ingest Rows per Instance
type: timeseries
description: Ingestion rate by row as in each frontend
unit: rowsps
queries:
- expr: sum by(instance, pod)(rate(greptime_table_operator_ingest_rows{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: Region Call QPS per Instance
type: timeseries
description: Region Call QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, request_type) (rate(greptime_grpc_region_request_count{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{request_type}}]'
- title: Region Call P99 per Instance
type: timeseries
description: Region Call P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, request_type) (rate(greptime_grpc_region_request_bucket{}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{request_type}}]'
- title: Mito Engine
panels:
- title: Request OPS per Instance
type: timeseries
description: Request QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, type) (rate(greptime_mito_handle_request_elapsed_count{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]'
- title: Request P99 per Instance
type: timeseries
description: Request P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, type) (rate(greptime_mito_handle_request_elapsed_bucket{}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]'
- title: Write Buffer per Instance
type: timeseries
description: Write Buffer per Instance.
unit: decbytes
queries:
- expr: greptime_mito_write_buffer_bytes{}
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: Write Rows per Instance
type: timeseries
description: Ingestion size by row counts.
unit: rowsps
queries:
- expr: sum by (instance, pod) (rate(greptime_mito_write_rows_total{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: Flush OPS per Instance
type: timeseries
description: Flush QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, reason) (rate(greptime_mito_flush_requests_total{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{reason}}]'
- title: Write Stall per Instance
type: timeseries
description: Write Stall per Instance.
queries:
- expr: sum by(instance, pod) (greptime_mito_write_stall_total{})
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: Read Stage OPS per Instance
type: timeseries
description: Read Stage OPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod) (rate(greptime_mito_read_stage_elapsed_count{ stage="total"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: Read Stage P99 per Instance
type: timeseries
description: Read Stage P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_read_stage_elapsed_bucket{}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]'
- title: Write Stage P99 per Instance
type: timeseries
description: Write Stage P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_write_stage_elapsed_bucket{}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]'
- title: Compaction OPS per Instance
type: timeseries
description: Compaction OPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod) (rate(greptime_mito_compaction_total_elapsed_count{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{ instance }}]-[{{pod}}]'
- title: Compaction P99 per Instance by Stage
type: timeseries
description: Compaction latency by stage
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, stage) (rate(greptime_mito_compaction_stage_elapsed_bucket{}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-p99'
- title: Compaction P99 per Instance
type: timeseries
description: Compaction P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le,stage) (rate(greptime_mito_compaction_total_elapsed_bucket{}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{stage}}]-compaction'
- title: WAL write size
type: timeseries
description: Write-ahead logs write size as bytes. This chart includes stats of p95 and p99 size by instance, total WAL write rate.
unit: bytes
queries:
- expr: histogram_quantile(0.95, sum by(le,instance, pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-req-size-p95'
- expr: histogram_quantile(0.99, sum by(le,instance,pod) (rate(raft_engine_write_size_bucket[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-req-size-p99'
- expr: sum by (instance, pod)(rate(raft_engine_write_size_sum[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-throughput'
- title: Cached Bytes per Instance
type: timeseries
description: Cached Bytes per Instance.
unit: decbytes
queries:
- expr: greptime_mito_cache_bytes{}
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]'
- title: Inflight Compaction
type: timeseries
description: Ongoing compaction task count
unit: none
queries:
- expr: greptime_mito_inflight_compaction_count
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: WAL sync duration seconds
type: timeseries
description: Raft engine (local disk) log store sync latency, p99
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(le, type, node, instance, pod) (rate(raft_engine_sync_log_duration_seconds_bucket[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-p99'
- title: Log Store op duration seconds
type: timeseries
description: Write-ahead log operations latency at p99
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(le,logstore,optype,instance, pod) (rate(greptime_logstore_op_elapsed_bucket[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{logstore}}]-[{{optype}}]-p99'
- title: Inflight Flush
type: timeseries
description: Ongoing flush task count
unit: none
queries:
- expr: greptime_mito_inflight_flush_count
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]'
- title: OpenDAL
panels:
- title: QPS per Instance
type: timeseries
description: QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
- title: Read QPS per Instance
type: timeseries
description: Read QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{ operation="read"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
- title: Read P99 per Instance
type: timeseries
description: Read P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{operation="read"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-{{scheme}}'
- title: Write QPS per Instance
type: timeseries
description: Write QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{ operation="write"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-{{scheme}}'
- title: Write P99 per Instance
type: timeseries
description: Write P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{ operation="write"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
- title: List QPS per Instance
type: timeseries
description: List QPS per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, scheme) (rate(opendal_operation_duration_seconds_count{ operation="list"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
- title: List P99 per Instance
type: timeseries
description: List P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme) (rate(opendal_operation_duration_seconds_bucket{ operation="list"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]'
- title: Other Requests per Instance
type: timeseries
description: Other Requests per Instance.
unit: ops
queries:
- expr: sum by(instance, pod, scheme, operation) (rate(opendal_operation_duration_seconds_count{operation!~"read|write|list|stat"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
- title: Other Request P99 per Instance
type: timeseries
description: Other Request P99 per Instance.
unit: s
queries:
- expr: histogram_quantile(0.99, sum by(instance, pod, le, scheme, operation) (rate(opendal_operation_duration_seconds_bucket{ operation!~"read|write|list"}[$__rate_interval])))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
- title: Opendal traffic
type: timeseries
description: Total traffic as in bytes by instance and operation
unit: decbytes
queries:
- expr: sum by(instance, pod, scheme, operation) (rate(opendal_operation_bytes_sum{}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]'
- title: OpenDAL errors per Instance
type: timeseries
description: OpenDAL error counts per Instance.
queries:
- expr: sum by(instance, pod, scheme, operation, error) (rate(opendal_operation_errors_total{ error!="NotFound"}[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{scheme}}]-[{{operation}}]-[{{error}}]'
- title: Metasrv
panels:
- title: Region migration datanode
type: state-timeline
description: Counter of region migration by source and destination
unit: none
queries:
- expr: greptime_meta_region_migration_stat{datanode_type="src"}
datasource:
type: prometheus
uid: ${metrics}
legendFormat: from-datanode-{{datanode_id}}
- expr: greptime_meta_region_migration_stat{datanode_type="desc"}
datasource:
type: prometheus
uid: ${metrics}
legendFormat: to-datanode-{{datanode_id}}
- title: Region migration error
type: timeseries
description: Counter of region migration error
unit: none
queries:
- expr: greptime_meta_region_migration_error
datasource:
type: prometheus
uid: ${metrics}
legendFormat: __auto
- title: Datanode load
type: timeseries
description: Gauge of load information of each datanode, collected via heartbeat between datanode and metasrv. This information is for metasrv to schedule workloads.
unit: none
queries:
- expr: greptime_datanode_load
datasource:
type: prometheus
uid: ${metrics}
legendFormat: __auto
- title: Flownode
panels:
- title: Flow Ingest / Output Rate
type: timeseries
description: Flow Ingest / Output Rate.
queries:
- expr: sum by(instance, pod, direction) (rate(greptime_flow_processed_rows[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{pod}}]-[{{instance}}]-[{{direction}}]'
- title: Flow Ingest Latency
type: timeseries
description: Flow Ingest Latency.
queries:
- expr: histogram_quantile(0.95, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-p95'
- expr: histogram_quantile(0.99, sum(rate(greptime_flow_insert_elapsed_bucket[$__rate_interval])) by (le, instance, pod))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-p99'
- title: Flow Operation Latency
type: timeseries
description: Flow Operation Latency.
queries:
- expr: histogram_quantile(0.95, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]-p95'
- expr: histogram_quantile(0.99, sum(rate(greptime_flow_processing_time_bucket[$__rate_interval])) by (le,instance,pod,type))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{type}}]-p99'
- title: Flow Buffer Size per Instance
type: timeseries
description: Flow Buffer Size per Instance.
queries:
- expr: greptime_flow_input_buf_size
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}]'
- title: Flow Processing Error per Instance
type: timeseries
description: Flow Processing Error per Instance.
queries:
- expr: sum by(instance,pod,code) (rate(greptime_flow_errors[$__rate_interval]))
datasource:
type: prometheus
uid: ${metrics}
legendFormat: '[{{instance}}]-[{{pod}}]-[{{code}}]'

File diff suppressed because it is too large Load Diff

File diff suppressed because it is too large Load Diff

54
grafana/scripts/check.sh Executable file
View File

@@ -0,0 +1,54 @@
#!/usr/bin/env bash
DASHBOARD_DIR=${1:-grafana/dashboards}
check_dashboard_description() {
for dashboard in $(find $DASHBOARD_DIR -name "*.json"); do
echo "Checking $dashboard description"
# Use jq to check for panels with empty or missing descriptions
invalid_panels=$(cat $dashboard | jq -r '
.panels[]
| select((.type == "stats" or .type == "timeseries") and (.description == "" or .description == null))')
# Check if any invalid panels were found
if [[ -n "$invalid_panels" ]]; then
echo "Error: The following panels have empty or missing descriptions:"
echo "$invalid_panels"
exit 1
else
echo "All panels with type 'stats' or 'timeseries' have valid descriptions."
fi
done
}
check_dashboards_generation() {
./grafana/scripts/gen-dashboards.sh
if [[ -n "$(git diff --name-only grafana/dashboards)" ]]; then
echo "Error: The dashboards are not generated correctly. You should execute the `make dashboards` command."
exit 1
fi
}
check_datasource() {
for dashboard in $(find $DASHBOARD_DIR -name "*.json"); do
echo "Checking $dashboard datasource"
jq -r '.panels[] | select(.type != "row") | .targets[] | [.datasource.type, .datasource.uid] | @tsv' $dashboard | while read -r type uid; do
# if the datasource is prometheus, check if the uid is ${metrics}
if [[ "$type" == "prometheus" && "$uid" != "\${metrics}" ]]; then
echo "Error: The datasource uid of $dashboard is not valid. It should be \${metrics}, got $uid"
exit 1
fi
# if the datasource is mysql, check if the uid is ${information_schema}
if [[ "$type" == "mysql" && "$uid" != "\${information_schema}" ]]; then
echo "Error: The datasource uid of $dashboard is not valid. It should be \${information_schema}, got $uid"
exit 1
fi
done
done
}
check_dashboards_generation
check_dashboard_description
check_datasource

View File

@@ -0,0 +1,25 @@
#! /usr/bin/env bash
CLUSTER_DASHBOARD_DIR=${1:-grafana/dashboards/cluster}
STANDALONE_DASHBOARD_DIR=${2:-grafana/dashboards/standalone}
DAC_IMAGE=ghcr.io/zyy17/dac:20250423-522bd35
remove_instance_filters() {
# Remove the instance filters for the standalone dashboards.
sed 's/instance=~\\"$datanode\\",//; s/instance=~\\"$datanode\\"//; s/instance=~\\"$frontend\\",//; s/instance=~\\"$frontend\\"//; s/instance=~\\"$metasrv\\",//; s/instance=~\\"$metasrv\\"//; s/instance=~\\"$flownode\\",//; s/instance=~\\"$flownode\\"//;' $CLUSTER_DASHBOARD_DIR/dashboard.json > $STANDALONE_DASHBOARD_DIR/dashboard.json
}
generate_intermediate_dashboards_and_docs() {
docker run -v ${PWD}:/greptimedb --rm ${DAC_IMAGE} \
-i /greptimedb/$CLUSTER_DASHBOARD_DIR/dashboard.json \
-o /greptimedb/$CLUSTER_DASHBOARD_DIR/dashboard.yaml \
-m /greptimedb/$CLUSTER_DASHBOARD_DIR/dashboard.md
docker run -v ${PWD}:/greptimedb --rm ${DAC_IMAGE} \
-i /greptimedb/$STANDALONE_DASHBOARD_DIR/dashboard.json \
-o /greptimedb/$STANDALONE_DASHBOARD_DIR/dashboard.yaml \
-m /greptimedb/$STANDALONE_DASHBOARD_DIR/dashboard.md
}
remove_instance_filters
generate_intermediate_dashboards_and_docs

View File

@@ -1,11 +0,0 @@
#!/usr/bin/env bash
BASEDIR=$(dirname "$0")
echo '| Title | Description | Expressions |
|---|---|---|'
cat $BASEDIR/greptimedb-cluster.json | jq -r '
.panels |
map(select(.type == "stat" or .type == "timeseries")) |
.[] | "| \(.title) | \(.description | gsub("\n"; "<br>")) | \(.targets | map(.expr // .rawSql | "`\(.|gsub("\n"; "<br>"))`") | join("<br>")) |"
'

View File

@@ -514,6 +514,7 @@ fn query_request_type(request: &QueryRequest) -> &'static str {
Some(Query::Sql(_)) => "query.sql",
Some(Query::LogicalPlan(_)) => "query.logical_plan",
Some(Query::PromRangeQuery(_)) => "query.prom_range",
Some(Query::InsertIntoPlan(_)) => "query.insert_into_plan",
None => "query.empty",
}
}

View File

@@ -27,7 +27,7 @@ use session::context::QueryContextRef;
use snafu::{ensure, OptionExt, ResultExt};
use table::metadata::TableType;
use table::table::adapter::DfTableProviderAdapter;
mod dummy_catalog;
pub mod dummy_catalog;
use dummy_catalog::DummyCatalogList;
use table::TableRef;

View File

@@ -36,8 +36,8 @@ use common_grpc::flight::{FlightDecoder, FlightMessage};
use common_query::Output;
use common_recordbatch::error::ExternalSnafu;
use common_recordbatch::RecordBatchStreamWrapper;
use common_telemetry::error;
use common_telemetry::tracing_context::W3cTrace;
use common_telemetry::{error, warn};
use futures::future;
use futures_util::{Stream, StreamExt, TryStreamExt};
use prost::Message;
@@ -192,6 +192,36 @@ impl Database {
from_grpc_response(response)
}
/// Retry if connection fails, max_retries is the max number of retries, so the total wait time
/// is `max_retries * GRPC_CONN_TIMEOUT`
pub async fn handle_with_retry(&self, request: Request, max_retries: u32) -> Result<u32> {
let mut client = make_database_client(&self.client)?.inner;
let mut retries = 0;
let request = self.to_rpc_request(request);
loop {
let raw_response = client.handle(request.clone()).await;
match (raw_response, retries < max_retries) {
(Ok(resp), _) => return from_grpc_response(resp.into_inner()),
(Err(err), true) => {
// determine if the error is retryable
if is_grpc_retryable(&err) {
// retry
retries += 1;
warn!("Retrying {} times with error = {:?}", retries, err);
continue;
}
}
(Err(err), false) => {
error!(
"Failed to send request to grpc handle after {} retries, error = {:?}",
retries, err
);
return Err(err.into());
}
}
}
}
#[inline]
fn to_rpc_request(&self, request: Request) -> GreptimeRequest {
GreptimeRequest {
@@ -368,6 +398,11 @@ impl Database {
}
}
/// by grpc standard, only `Unavailable` is retryable, see: https://github.com/grpc/grpc/blob/master/doc/statuscodes.md#status-codes-and-their-use-in-grpc
pub fn is_grpc_retryable(err: &tonic::Status) -> bool {
matches!(err.code(), tonic::Code::Unavailable)
}
#[derive(Default, Debug, Clone)]
struct FlightContext {
auth_header: Option<AuthHeader>,

View File

@@ -78,13 +78,6 @@ pub enum Error {
source: datanode::error::Error,
},
#[snafu(display("Failed to build object storage manager"))]
BuildObjectStorageManager {
#[snafu(implicit)]
location: Location,
source: datanode::error::Error,
},
#[snafu(display("Failed to shutdown datanode"))]
ShutdownDatanode {
#[snafu(implicit)]
@@ -335,8 +328,6 @@ impl ErrorExt for Error {
source.status_code()
}
Error::BuildObjectStorageManager { source, .. } => source.status_code(),
Error::MissingConfig { .. }
| Error::LoadLayeredConfig { .. }
| Error::IllegalConfig { .. }

View File

@@ -345,7 +345,7 @@ impl StartCommand {
let client = Arc::new(NodeClients::new(channel_config));
let invoker = FrontendInvoker::build_from(
flownode.flow_worker_manager().clone(),
flownode.flow_engine().streaming_engine(),
catalog_manager.clone(),
cached_meta_backend.clone(),
layered_cache_registry.clone(),
@@ -355,7 +355,9 @@ impl StartCommand {
.await
.context(StartFlownodeSnafu)?;
flownode
.flow_worker_manager()
.flow_engine()
.streaming_engine()
// TODO(discord9): refactor and avoid circular reference
.set_frontend_invoker(invoker)
.await;

View File

@@ -12,6 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
use std::fmt;
use std::time::Duration;
use async_trait::async_trait;
@@ -131,8 +132,8 @@ impl SubCommand {
}
}
#[derive(Debug, Default, Parser)]
struct StartCommand {
#[derive(Default, Parser)]
pub struct StartCommand {
/// The address to bind the gRPC server.
#[clap(long, alias = "bind-addr")]
rpc_bind_addr: Option<String>,
@@ -171,8 +172,29 @@ struct StartCommand {
backend: Option<BackendImpl>,
}
impl fmt::Debug for StartCommand {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
f.debug_struct("StartCommand")
.field("rpc_bind_addr", &self.rpc_bind_addr)
.field("rpc_server_addr", &self.rpc_server_addr)
.field("store_addrs", &self.sanitize_store_addrs())
.field("config_file", &self.config_file)
.field("selector", &self.selector)
.field("use_memory_store", &self.use_memory_store)
.field("enable_region_failover", &self.enable_region_failover)
.field("http_addr", &self.http_addr)
.field("http_timeout", &self.http_timeout)
.field("env_prefix", &self.env_prefix)
.field("data_home", &self.data_home)
.field("store_key_prefix", &self.store_key_prefix)
.field("max_txn_ops", &self.max_txn_ops)
.field("backend", &self.backend)
.finish()
}
}
impl StartCommand {
fn load_options(&self, global_options: &GlobalOptions) -> Result<MetasrvOptions> {
pub fn load_options(&self, global_options: &GlobalOptions) -> Result<MetasrvOptions> {
let mut opts = MetasrvOptions::load_layered_options(
self.config_file.as_deref(),
self.env_prefix.as_ref(),
@@ -184,6 +206,15 @@ impl StartCommand {
Ok(opts)
}
fn sanitize_store_addrs(&self) -> Option<Vec<String>> {
self.store_addrs.as_ref().map(|addrs| {
addrs
.iter()
.map(|addr| common_meta::kv_backend::util::sanitize_connection_string(addr))
.collect()
})
}
// The precedence order is: cli > config file > environment variables > default values.
fn merge_with_cli_options(
&self,
@@ -261,7 +292,7 @@ impl StartCommand {
Ok(())
}
async fn build(&self, opts: MetasrvOptions) -> Result<Instance> {
pub async fn build(&self, opts: MetasrvOptions) -> Result<Instance> {
common_runtime::init_global_runtimes(&opts.runtime);
let guard = common_telemetry::init_global_logging(

View File

@@ -44,7 +44,6 @@ use common_meta::peer::Peer;
use common_meta::region_keeper::MemoryRegionKeeper;
use common_meta::region_registry::LeaderRegionRegistry;
use common_meta::sequence::SequenceBuilder;
use common_meta::snapshot::MetadataSnapshotManager;
use common_meta::wal_options_allocator::{build_wal_options_allocator, WalOptionsAllocatorRef};
use common_procedure::{ProcedureInfo, ProcedureManagerRef};
use common_telemetry::info;
@@ -57,8 +56,8 @@ use datanode::datanode::{Datanode, DatanodeBuilder};
use datanode::region_server::RegionServer;
use file_engine::config::EngineConfig as FileEngineConfig;
use flow::{
FlowConfig, FlowWorkerManager, FlownodeBuilder, FlownodeInstance, FlownodeOptions,
FrontendClient, FrontendInvoker,
FlowConfig, FlownodeBuilder, FlownodeInstance, FlownodeOptions, FrontendClient,
FrontendInvoker, GrpcQueryHandlerWithBoxedError, StreamingEngine,
};
use frontend::frontend::{Frontend, FrontendOptions};
use frontend::instance::builder::FrontendBuilder;
@@ -498,10 +497,6 @@ impl StartCommand {
.build(),
);
let object_store_manager = DatanodeBuilder::build_object_store_manager(&dn_opts.storage)
.await
.context(error::BuildObjectStorageManagerSnafu)?;
let datanode = DatanodeBuilder::new(dn_opts, plugins.clone(), Mode::Standalone)
.with_kv_backend(kv_backend.clone())
.with_cache_registry(layered_cache_registry.clone())
@@ -529,17 +524,17 @@ impl StartCommand {
..Default::default()
};
// TODO(discord9): for standalone not use grpc, but just somehow get a handler to frontend grpc client without
// for standalone not use grpc, but get a handler to frontend grpc client without
// actually make a connection
let fe_server_addr = fe_opts.grpc.bind_addr.clone();
let frontend_client = FrontendClient::from_static_grpc_addr(fe_server_addr);
let (frontend_client, frontend_instance_handler) =
FrontendClient::from_empty_grpc_handler();
let flow_builder = FlownodeBuilder::new(
flownode_options,
plugins.clone(),
table_metadata_manager.clone(),
catalog_manager.clone(),
flow_metadata_manager.clone(),
Arc::new(frontend_client),
Arc::new(frontend_client.clone()),
);
let flownode = flow_builder
.build()
@@ -549,15 +544,15 @@ impl StartCommand {
// set the ref to query for the local flow state
{
let flow_worker_manager = flownode.flow_worker_manager();
let flow_streaming_engine = flownode.flow_engine().streaming_engine();
information_extension
.set_flow_worker_manager(flow_worker_manager.clone())
.set_flow_streaming_engine(flow_streaming_engine)
.await;
}
let node_manager = Arc::new(StandaloneDatanodeManager {
region_server: datanode.region_server(),
flow_server: flownode.flow_worker_manager(),
flow_server: flownode.flow_engine(),
});
let table_id_sequence = Arc::new(
@@ -596,11 +591,6 @@ impl StartCommand {
)
.await?;
let metadata_snapshot_manager = MetadataSnapshotManager::new(
kv_backend.clone(),
object_store_manager.default_object_store().clone(),
);
let fe_instance = FrontendBuilder::new(
fe_opts.clone(),
kv_backend.clone(),
@@ -611,16 +601,24 @@ impl StartCommand {
StatementStatistics::new(opts.logging.slow_query.clone()),
)
.with_plugin(plugins.clone())
.with_metadata_snapshot_manager(metadata_snapshot_manager)
.try_build()
.await
.context(error::StartFrontendSnafu)?;
let fe_instance = Arc::new(fe_instance);
let flow_worker_manager = flownode.flow_worker_manager();
// set the frontend client for flownode
let grpc_handler = fe_instance.clone() as Arc<dyn GrpcQueryHandlerWithBoxedError>;
let weak_grpc_handler = Arc::downgrade(&grpc_handler);
frontend_instance_handler
.lock()
.unwrap()
.replace(weak_grpc_handler);
// set the frontend invoker for flownode
let flow_streaming_engine = flownode.flow_engine().streaming_engine();
// flow server need to be able to use frontend to write insert requests back
let invoker = FrontendInvoker::build_from(
flow_worker_manager.clone(),
flow_streaming_engine.clone(),
catalog_manager.clone(),
kv_backend.clone(),
layered_cache_registry.clone(),
@@ -629,7 +627,7 @@ impl StartCommand {
)
.await
.context(error::StartFlownodeSnafu)?;
flow_worker_manager.set_frontend_invoker(invoker).await;
flow_streaming_engine.set_frontend_invoker(invoker).await;
let export_metrics_task = ExportMetricsTask::try_new(&opts.export_metrics, Some(&plugins))
.context(error::ServersSnafu)?;
@@ -705,7 +703,7 @@ pub struct StandaloneInformationExtension {
region_server: RegionServer,
procedure_manager: ProcedureManagerRef,
start_time_ms: u64,
flow_worker_manager: RwLock<Option<Arc<FlowWorkerManager>>>,
flow_streaming_engine: RwLock<Option<Arc<StreamingEngine>>>,
}
impl StandaloneInformationExtension {
@@ -714,14 +712,14 @@ impl StandaloneInformationExtension {
region_server,
procedure_manager,
start_time_ms: common_time::util::current_time_millis() as u64,
flow_worker_manager: RwLock::new(None),
flow_streaming_engine: RwLock::new(None),
}
}
/// Set the flow worker manager for the standalone instance.
pub async fn set_flow_worker_manager(&self, flow_worker_manager: Arc<FlowWorkerManager>) {
let mut guard = self.flow_worker_manager.write().await;
*guard = Some(flow_worker_manager);
/// Set the flow streaming engine for the standalone instance.
pub async fn set_flow_streaming_engine(&self, flow_streaming_engine: Arc<StreamingEngine>) {
let mut guard = self.flow_streaming_engine.write().await;
*guard = Some(flow_streaming_engine);
}
}
@@ -800,7 +798,7 @@ impl InformationExtension for StandaloneInformationExtension {
async fn flow_stats(&self) -> std::result::Result<Option<FlowStat>, Self::Error> {
Ok(Some(
self.flow_worker_manager
self.flow_streaming_engine
.read()
.await
.as_ref()

View File

@@ -74,6 +74,7 @@ fn test_load_datanode_example_config() {
RegionEngineConfig::File(FileEngineConfig {}),
RegionEngineConfig::Metric(MetricEngineConfig {
experimental_sparse_primary_key_encoding: false,
flush_metadata_region_interval: Duration::from_secs(30),
}),
],
logging: LoggingOptions {
@@ -216,6 +217,7 @@ fn test_load_standalone_example_config() {
RegionEngineConfig::File(FileEngineConfig {}),
RegionEngineConfig::Metric(MetricEngineConfig {
experimental_sparse_primary_key_encoding: false,
flush_metadata_region_interval: Duration::from_secs(30),
}),
],
storage: StorageConfig {

View File

@@ -31,7 +31,8 @@ impl Plugins {
}
pub fn insert<T: 'static + Send + Sync>(&self, value: T) {
let _ = self.write().insert(value);
let last = self.write().insert(value);
assert!(last.is_none(), "each type of plugins must be one and only");
}
pub fn get<T: 'static + Send + Sync + Clone>(&self) -> Option<T> {
@@ -137,4 +138,12 @@ mod tests {
assert_eq!(plugins.len(), 2);
assert!(!plugins.is_empty());
}
#[test]
#[should_panic(expected = "each type of plugins must be one and only")]
fn test_plugin_uniqueness() {
let plugins = Plugins::new();
plugins.insert(1i32);
plugins.insert(2i32);
}
}

View File

@@ -15,7 +15,6 @@
mod add_region_follower;
mod flush_compact_region;
mod flush_compact_table;
mod metadata_snaphost;
mod migrate_region;
mod remove_region_follower;
@@ -24,7 +23,6 @@ use std::sync::Arc;
use add_region_follower::AddRegionFollowerFunction;
use flush_compact_region::{CompactRegionFunction, FlushRegionFunction};
use flush_compact_table::{CompactTableFunction, FlushTableFunction};
use metadata_snaphost::{DumpMetadataFunction, RestoreMetadataFunction};
use migrate_region::MigrateRegionFunction;
use remove_region_follower::RemoveRegionFollowerFunction;
@@ -45,7 +43,5 @@ impl AdminFunction {
registry.register_async(Arc::new(FlushTableFunction));
registry.register_async(Arc::new(CompactTableFunction));
registry.register_async(Arc::new(FlushFlowFunction));
registry.register_async(Arc::new(DumpMetadataFunction));
registry.register_async(Arc::new(RestoreMetadataFunction));
}
}

View File

@@ -1,56 +0,0 @@
use common_macro::admin_fn;
use common_query::error::{MissingMetadataSnapshotHandlerSnafu, Result};
use common_query::prelude::{Signature, Volatility};
use datatypes::prelude::*;
use session::context::QueryContextRef;
use crate::handlers::MetadataSnapshotHandlerRef;
const METADATA_DIR: &str = "/snaphost/";
const METADATA_FILE_NAME: &str = "dump_metadata";
const METADATA_FILE_EXTENSION: &str = "metadata.fb";
#[admin_fn(
name = DumpMetadataFunction,
display_name = dump_metadata,
sig_fn = dump_signature,
ret = string
)]
pub(crate) async fn dump_metadata(
metadata_snapshot_handler: &MetadataSnapshotHandlerRef,
_query_ctx: &QueryContextRef,
_params: &[ValueRef<'_>],
) -> Result<Value> {
let filename = metadata_snapshot_handler
.dump(METADATA_DIR, METADATA_FILE_NAME)
.await?;
Ok(Value::from(filename))
}
fn dump_signature() -> Signature {
Signature::uniform(0, vec![], Volatility::Immutable)
}
#[admin_fn(
name = RestoreMetadataFunction,
display_name = restore_metadata,
sig_fn = restore_signature,
ret = uint64,
)]
pub(crate) async fn restore_metadata(
metadata_snapshot_handler: &MetadataSnapshotHandlerRef,
_query_ctx: &QueryContextRef,
_params: &[ValueRef<'_>],
) -> Result<Value> {
let num_keyvalues = metadata_snapshot_handler
.restore(
METADATA_DIR,
&format!("{METADATA_FILE_NAME}.{METADATA_FILE_EXTENSION}"),
)
.await?;
Ok(Value::from(num_keyvalues))
}
fn restore_signature() -> Signature {
Signature::uniform(0, vec![], Volatility::Immutable)
}

View File

@@ -89,18 +89,8 @@ pub trait FlowServiceHandler: Send + Sync {
) -> Result<api::v1::flow::FlowResponse>;
}
/// This metadata snapshot handler is only use for dump and restore metadata for now.
#[async_trait]
pub trait MetadataSnapshotHandler: Send + Sync {
async fn dump(&self, path: &str, filename: &str) -> Result<String>;
async fn restore(&self, path: &str, filename: &str) -> Result<u64>;
}
pub type TableMutationHandlerRef = Arc<dyn TableMutationHandler>;
pub type ProcedureServiceHandlerRef = Arc<dyn ProcedureServiceHandler>;
pub type FlowServiceHandlerRef = Arc<dyn FlowServiceHandler>;
pub type MetadataSnapshotHandlerRef = Arc<dyn MetadataSnapshotHandler>;

View File

@@ -13,10 +13,8 @@
// limitations under the License.
use std::sync::Arc;
mod greatest;
mod to_unixtime;
use greatest::GreatestFunction;
use to_unixtime::ToUnixtimeFunction;
use crate::function_registry::FunctionRegistry;
@@ -26,6 +24,5 @@ pub(crate) struct TimestampFunction;
impl TimestampFunction {
pub fn register(registry: &FunctionRegistry) {
registry.register(Arc::new(ToUnixtimeFunction));
registry.register(Arc::new(GreatestFunction));
}
}

View File

@@ -1,328 +0,0 @@
// Copyright 2023 Greptime Team
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
use std::fmt::{self};
use common_query::error::{
self, ArrowComputeSnafu, InvalidFuncArgsSnafu, Result, UnsupportedInputDataTypeSnafu,
};
use common_query::prelude::{Signature, Volatility};
use datafusion::arrow::compute::kernels::cmp::gt;
use datatypes::arrow::array::AsArray;
use datatypes::arrow::compute::cast;
use datatypes::arrow::compute::kernels::zip;
use datatypes::arrow::datatypes::{
DataType as ArrowDataType, Date32Type, TimeUnit, TimestampMicrosecondType,
TimestampMillisecondType, TimestampNanosecondType, TimestampSecondType,
};
use datatypes::prelude::ConcreteDataType;
use datatypes::types::TimestampType;
use datatypes::vectors::{Helper, VectorRef};
use snafu::{ensure, ResultExt};
use crate::function::{Function, FunctionContext};
#[derive(Clone, Debug, Default)]
pub struct GreatestFunction;
const NAME: &str = "greatest";
macro_rules! gt_time_types {
($ty: ident, $columns:expr) => {{
let column1 = $columns[0].to_arrow_array();
let column2 = $columns[1].to_arrow_array();
let column1 = column1.as_primitive::<$ty>();
let column2 = column2.as_primitive::<$ty>();
let boolean_array = gt(&column1, &column2).context(ArrowComputeSnafu)?;
let result = zip::zip(&boolean_array, &column1, &column2).context(ArrowComputeSnafu)?;
Helper::try_into_vector(&result).context(error::FromArrowArraySnafu)
}};
}
impl Function for GreatestFunction {
fn name(&self) -> &str {
NAME
}
fn return_type(&self, input_types: &[ConcreteDataType]) -> Result<ConcreteDataType> {
ensure!(
input_types.len() == 2,
InvalidFuncArgsSnafu {
err_msg: format!(
"The length of the args is not correct, expect exactly two, have: {}",
input_types.len()
)
}
);
match &input_types[0] {
ConcreteDataType::String(_) => Ok(ConcreteDataType::timestamp_millisecond_datatype()),
ConcreteDataType::Date(_) => Ok(ConcreteDataType::date_datatype()),
ConcreteDataType::Timestamp(ts_type) => Ok(ConcreteDataType::Timestamp(*ts_type)),
_ => UnsupportedInputDataTypeSnafu {
function: NAME,
datatypes: input_types,
}
.fail(),
}
}
fn signature(&self) -> Signature {
Signature::uniform(
2,
vec![
ConcreteDataType::string_datatype(),
ConcreteDataType::date_datatype(),
ConcreteDataType::timestamp_nanosecond_datatype(),
ConcreteDataType::timestamp_microsecond_datatype(),
ConcreteDataType::timestamp_millisecond_datatype(),
ConcreteDataType::timestamp_second_datatype(),
],
Volatility::Immutable,
)
}
fn eval(&self, _func_ctx: &FunctionContext, columns: &[VectorRef]) -> Result<VectorRef> {
ensure!(
columns.len() == 2,
InvalidFuncArgsSnafu {
err_msg: format!(
"The length of the args is not correct, expect exactly two, have: {}",
columns.len()
),
}
);
match columns[0].data_type() {
ConcreteDataType::String(_) => {
let column1 = cast(
&columns[0].to_arrow_array(),
&ArrowDataType::Timestamp(TimeUnit::Millisecond, None),
)
.context(ArrowComputeSnafu)?;
let column1 = column1.as_primitive::<TimestampMillisecondType>();
let column2 = cast(
&columns[1].to_arrow_array(),
&ArrowDataType::Timestamp(TimeUnit::Millisecond, None),
)
.context(ArrowComputeSnafu)?;
let column2 = column2.as_primitive::<TimestampMillisecondType>();
let boolean_array = gt(&column1, &column2).context(ArrowComputeSnafu)?;
let result =
zip::zip(&boolean_array, &column1, &column2).context(ArrowComputeSnafu)?;
Ok(Helper::try_into_vector(&result).context(error::FromArrowArraySnafu)?)
}
ConcreteDataType::Date(_) => gt_time_types!(Date32Type, columns),
ConcreteDataType::Timestamp(ts_type) => match ts_type {
TimestampType::Second(_) => gt_time_types!(TimestampSecondType, columns),
TimestampType::Millisecond(_) => {
gt_time_types!(TimestampMillisecondType, columns)
}
TimestampType::Microsecond(_) => {
gt_time_types!(TimestampMicrosecondType, columns)
}
TimestampType::Nanosecond(_) => {
gt_time_types!(TimestampNanosecondType, columns)
}
},
_ => UnsupportedInputDataTypeSnafu {
function: NAME,
datatypes: columns.iter().map(|c| c.data_type()).collect::<Vec<_>>(),
}
.fail(),
}
}
}
impl fmt::Display for GreatestFunction {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
write!(f, "GREATEST")
}
}
#[cfg(test)]
mod tests {
use std::sync::Arc;
use common_time::timestamp::TimeUnit;
use common_time::{Date, Timestamp};
use datatypes::types::{
DateType, TimestampMicrosecondType, TimestampMillisecondType, TimestampNanosecondType,
TimestampSecondType,
};
use datatypes::value::Value;
use datatypes::vectors::{
DateVector, StringVector, TimestampMicrosecondVector, TimestampMillisecondVector,
TimestampNanosecondVector, TimestampSecondVector, Vector,
};
use paste::paste;
use super::*;
#[test]
fn test_greatest_takes_string_vector() {
let function = GreatestFunction;
assert_eq!(
function
.return_type(&[
ConcreteDataType::string_datatype(),
ConcreteDataType::string_datatype()
])
.unwrap(),
ConcreteDataType::timestamp_millisecond_datatype()
);
let columns = vec![
Arc::new(StringVector::from(vec![
"1970-01-01".to_string(),
"2012-12-23".to_string(),
])) as _,
Arc::new(StringVector::from(vec![
"2001-02-01".to_string(),
"1999-01-01".to_string(),
])) as _,
];
let result = function
.eval(&FunctionContext::default(), &columns)
.unwrap();
let result = result
.as_any()
.downcast_ref::<TimestampMillisecondVector>()
.unwrap();
assert_eq!(result.len(), 2);
assert_eq!(
result.get(0),
Value::Timestamp(Timestamp::from_str("2001-02-01 00:00:00", None).unwrap())
);
assert_eq!(
result.get(1),
Value::Timestamp(Timestamp::from_str("2012-12-23 00:00:00", None).unwrap())
);
}
#[test]
fn test_greatest_takes_date_vector() {
let function = GreatestFunction;
assert_eq!(
function
.return_type(&[
ConcreteDataType::date_datatype(),
ConcreteDataType::date_datatype()
])
.unwrap(),
ConcreteDataType::Date(DateType)
);
let columns = vec![
Arc::new(DateVector::from_slice(vec![-1, 2])) as _,
Arc::new(DateVector::from_slice(vec![0, 1])) as _,
];
let result = function
.eval(&FunctionContext::default(), &columns)
.unwrap();
let result = result.as_any().downcast_ref::<DateVector>().unwrap();
assert_eq!(result.len(), 2);
assert_eq!(
result.get(0),
Value::Date(Date::from_str_utc("1970-01-01").unwrap())
);
assert_eq!(
result.get(1),
Value::Date(Date::from_str_utc("1970-01-03").unwrap())
);
}
#[test]
fn test_greatest_takes_datetime_vector() {
let function = GreatestFunction;
assert_eq!(
function
.return_type(&[
ConcreteDataType::timestamp_millisecond_datatype(),
ConcreteDataType::timestamp_millisecond_datatype()
])
.unwrap(),
ConcreteDataType::timestamp_millisecond_datatype()
);
let columns = vec![
Arc::new(TimestampMillisecondVector::from_slice(vec![-1, 2])) as _,
Arc::new(TimestampMillisecondVector::from_slice(vec![0, 1])) as _,
];
let result = function
.eval(&FunctionContext::default(), &columns)
.unwrap();
let result = result
.as_any()
.downcast_ref::<TimestampMillisecondVector>()
.unwrap();
assert_eq!(result.len(), 2);
assert_eq!(
result.get(0),
Value::Timestamp(Timestamp::from_str("1970-01-01 00:00:00", None).unwrap())
);
assert_eq!(
result.get(1),
Value::Timestamp(Timestamp::from_str("1970-01-01 00:00:00.002", None).unwrap())
);
}
macro_rules! test_timestamp {
($type: expr,$unit: ident) => {
paste! {
#[test]
fn [<test_greatest_takes_ $unit:lower _vector>]() {
let function = GreatestFunction;
assert_eq!(
function.return_type(&[$type, $type]).unwrap(),
ConcreteDataType::Timestamp(TimestampType::$unit([<Timestamp $unit Type>]))
);
let columns = vec![
Arc::new([<Timestamp $unit Vector>]::from_slice(vec![-1, 2])) as _,
Arc::new([<Timestamp $unit Vector>]::from_slice(vec![0, 1])) as _,
];
let result = function.eval(&FunctionContext::default(), &columns).unwrap();
let result = result.as_any().downcast_ref::<[<Timestamp $unit Vector>]>().unwrap();
assert_eq!(result.len(), 2);
assert_eq!(
result.get(0),
Value::Timestamp(Timestamp::new(0, TimeUnit::$unit))
);
assert_eq!(
result.get(1),
Value::Timestamp(Timestamp::new(2, TimeUnit::$unit))
);
}
}
}
}
test_timestamp!(
ConcreteDataType::timestamp_nanosecond_datatype(),
Nanosecond
);
test_timestamp!(
ConcreteDataType::timestamp_microsecond_datatype(),
Microsecond
);
test_timestamp!(
ConcreteDataType::timestamp_millisecond_datatype(),
Millisecond
);
test_timestamp!(ConcreteDataType::timestamp_second_datatype(), Second);
}

View File

@@ -115,6 +115,13 @@ impl Function for UddSketchCalcFunction {
}
};
// Check if the sketch is empty, if so, return null
// This is important to avoid panics when calling estimate_quantile on an empty sketch
// In practice, this will happen if input is all null
if sketch.bucket_iter().count() == 0 {
builder.push_null();
continue;
}
// Compute the estimated quantile from the sketch
let result = sketch.estimate_quantile(perc);
builder.push(Some(result));

View File

@@ -12,10 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
use crate::handlers::{
FlowServiceHandlerRef, MetadataSnapshotHandlerRef, ProcedureServiceHandlerRef,
TableMutationHandlerRef,
};
use crate::handlers::{FlowServiceHandlerRef, ProcedureServiceHandlerRef, TableMutationHandlerRef};
/// Shared state for SQL functions.
/// The handlers in state may be `None` in cli command-line or test cases.
@@ -27,8 +24,6 @@ pub struct FunctionState {
pub procedure_service_handler: Option<ProcedureServiceHandlerRef>,
// The flownode handler
pub flow_service_handler: Option<FlowServiceHandlerRef>,
// The metadata snapshot handler
pub metadata_snapshot_handler: Option<MetadataSnapshotHandlerRef>,
}
impl FunctionState {
@@ -53,14 +48,10 @@ impl FunctionState {
CompactTableRequest, DeleteRequest, FlushTableRequest, InsertRequest,
};
use crate::handlers::{
FlowServiceHandler, MetadataSnapshotHandler, ProcedureServiceHandler,
TableMutationHandler,
};
use crate::handlers::{FlowServiceHandler, ProcedureServiceHandler, TableMutationHandler};
struct MockProcedureServiceHandler;
struct MockTableMutationHandler;
struct MockFlowServiceHandler;
struct MockMetadataServiceHandler;
const ROWS: usize = 42;
#[async_trait]
@@ -159,22 +150,10 @@ impl FunctionState {
}
}
#[async_trait]
impl MetadataSnapshotHandler for MockMetadataServiceHandler {
async fn dump(&self, _path: &str, _filename: &str) -> Result<String> {
Ok("test_filename".to_string())
}
async fn restore(&self, _path: &str, _filename: &str) -> Result<u64> {
Ok(100)
}
}
Self {
table_mutation_handler: Some(Arc::new(MockTableMutationHandler)),
procedure_service_handler: Some(Arc::new(MockProcedureServiceHandler)),
flow_service_handler: Some(Arc::new(MockFlowServiceHandler)),
metadata_snapshot_handler: Some(Arc::new(MockMetadataServiceHandler)),
}
}
}

View File

@@ -18,4 +18,5 @@ pub mod flight;
pub mod precision;
pub mod select;
pub use arrow_flight::FlightData;
pub use error::Error;

View File

@@ -179,10 +179,6 @@ fn build_struct(
Ident::new("flow_service_handler", handler_type.span()),
Ident::new("MissingFlowServiceHandlerSnafu", handler_type.span()),
),
"MetadataSnapshotHandlerRef" => (
Ident::new("metadata_snapshot_handler", handler_type.span()),
Ident::new("MissingMetadataSnapshotHandlerSnafu", handler_type.span()),
),
handler => ok!(error!(
handler_type.span(),
format!("Unknown handler type: {handler}")

View File

@@ -41,7 +41,6 @@ deadpool = { workspace = true, optional = true }
deadpool-postgres = { workspace = true, optional = true }
derive_builder.workspace = true
etcd-client.workspace = true
flexbuffers = "25.2"
futures.workspace = true
futures-util.workspace = true
hex.workspace = true
@@ -49,7 +48,6 @@ humantime-serde.workspace = true
itertools.workspace = true
lazy_static.workspace = true
moka.workspace = true
object-store.workspace = true
prometheus.workspace = true
prost.workspace = true
rand.workspace = true
@@ -72,7 +70,6 @@ typetag.workspace = true
[dev-dependencies]
chrono.workspace = true
common-procedure = { workspace = true, features = ["testing"] }
common-test-util.workspace = true
common-wal = { workspace = true, features = ["testing"] }
datatypes.workspace = true
hyper = { version = "0.14", features = ["full"] }

View File

@@ -24,21 +24,39 @@ use crate::cache::{CacheContainer, Initializer};
use crate::error::Result;
use crate::instruction::{CacheIdent, CreateFlow, DropFlow};
use crate::key::flow::{TableFlowManager, TableFlowManagerRef};
use crate::key::{FlowId, FlowPartitionId};
use crate::kv_backend::KvBackendRef;
use crate::peer::Peer;
use crate::FlownodeId;
type FlownodeSet = Arc<HashMap<FlownodeId, Peer>>;
/// Flow id&flow partition key
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub struct FlowIdent {
pub flow_id: FlowId,
pub partition_id: FlowPartitionId,
}
impl FlowIdent {
pub fn new(flow_id: FlowId, partition_id: FlowPartitionId) -> Self {
Self {
flow_id,
partition_id,
}
}
}
/// cache for TableFlowManager, the table_id part is in the outer cache
/// include flownode_id, flow_id, partition_id mapping to Peer
type FlownodeFlowSet = Arc<HashMap<FlowIdent, Peer>>;
pub type TableFlownodeSetCacheRef = Arc<TableFlownodeSetCache>;
/// [TableFlownodeSetCache] caches the [TableId] to [FlownodeSet] mapping.
pub type TableFlownodeSetCache = CacheContainer<TableId, FlownodeSet, CacheIdent>;
pub type TableFlownodeSetCache = CacheContainer<TableId, FlownodeFlowSet, CacheIdent>;
/// Constructs a [TableFlownodeSetCache].
pub fn new_table_flownode_set_cache(
name: String,
cache: Cache<TableId, FlownodeSet>,
cache: Cache<TableId, FlownodeFlowSet>,
kv_backend: KvBackendRef,
) -> TableFlownodeSetCache {
let table_flow_manager = Arc::new(TableFlowManager::new(kv_backend));
@@ -47,7 +65,7 @@ pub fn new_table_flownode_set_cache(
CacheContainer::new(name, cache, Box::new(invalidator), init, filter)
}
fn init_factory(table_flow_manager: TableFlowManagerRef) -> Initializer<TableId, FlownodeSet> {
fn init_factory(table_flow_manager: TableFlowManagerRef) -> Initializer<TableId, FlownodeFlowSet> {
Arc::new(move |&table_id| {
let table_flow_manager = table_flow_manager.clone();
Box::pin(async move {
@@ -57,7 +75,12 @@ fn init_factory(table_flow_manager: TableFlowManagerRef) -> Initializer<TableId,
.map(|flows| {
flows
.into_iter()
.map(|(key, value)| (key.flownode_id(), value.peer))
.map(|(key, value)| {
(
FlowIdent::new(key.flow_id(), key.partition_id()),
value.peer,
)
})
.collect::<HashMap<_, _>>()
})
// We must cache the `HashSet` even if it's empty,
@@ -71,26 +94,33 @@ fn init_factory(table_flow_manager: TableFlowManagerRef) -> Initializer<TableId,
}
async fn handle_create_flow(
cache: &Cache<TableId, FlownodeSet>,
cache: &Cache<TableId, FlownodeFlowSet>,
CreateFlow {
flow_id,
source_table_ids,
flownodes: flownode_peers,
partition_to_peer_mapping: flow_part2nodes,
}: &CreateFlow,
) {
for table_id in source_table_ids {
let entry = cache.entry(*table_id);
entry
.and_compute_with(
async |entry: Option<moka::Entry<u32, Arc<HashMap<u64, _>>>>| match entry {
async |entry: Option<moka::Entry<u32, FlownodeFlowSet>>| match entry {
Some(entry) => {
let mut map = entry.into_value().as_ref().clone();
map.extend(flownode_peers.iter().map(|peer| (peer.id, peer.clone())));
map.extend(
flow_part2nodes.iter().map(|(part, peer)| {
(FlowIdent::new(*flow_id, *part), peer.clone())
}),
);
Op::Put(Arc::new(map))
}
None => Op::Put(Arc::new(HashMap::from_iter(
flownode_peers.iter().map(|peer| (peer.id, peer.clone())),
))),
None => {
Op::Put(Arc::new(HashMap::from_iter(flow_part2nodes.iter().map(
|(part, peer)| (FlowIdent::new(*flow_id, *part), peer.clone()),
))))
}
},
)
.await;
@@ -98,21 +128,23 @@ async fn handle_create_flow(
}
async fn handle_drop_flow(
cache: &Cache<TableId, FlownodeSet>,
cache: &Cache<TableId, FlownodeFlowSet>,
DropFlow {
flow_id,
source_table_ids,
flownode_ids,
flow_part2node_id,
}: &DropFlow,
) {
for table_id in source_table_ids {
let entry = cache.entry(*table_id);
entry
.and_compute_with(
async |entry: Option<moka::Entry<u32, Arc<HashMap<u64, _>>>>| match entry {
async |entry: Option<moka::Entry<u32, FlownodeFlowSet>>| match entry {
Some(entry) => {
let mut set = entry.into_value().as_ref().clone();
for flownode_id in flownode_ids {
set.remove(flownode_id);
for (part, _node) in flow_part2node_id {
let key = FlowIdent::new(*flow_id, *part);
set.remove(&key);
}
Op::Put(Arc::new(set))
@@ -128,7 +160,7 @@ async fn handle_drop_flow(
}
fn invalidator<'a>(
cache: &'a Cache<TableId, FlownodeSet>,
cache: &'a Cache<TableId, FlownodeFlowSet>,
ident: &'a CacheIdent,
) -> BoxFuture<'a, Result<()>> {
Box::pin(async move {
@@ -154,7 +186,7 @@ mod tests {
use moka::future::CacheBuilder;
use table::table_name::TableName;
use crate::cache::flow::table_flownode::new_table_flownode_set_cache;
use crate::cache::flow::table_flownode::{new_table_flownode_set_cache, FlowIdent};
use crate::instruction::{CacheIdent, CreateFlow, DropFlow};
use crate::key::flow::flow_info::FlowInfoValue;
use crate::key::flow::flow_route::FlowRouteValue;
@@ -187,6 +219,7 @@ mod tests {
},
flownode_ids: BTreeMap::from([(0, 1), (1, 2), (2, 3)]),
catalog_name: DEFAULT_CATALOG_NAME.to_string(),
query_context: None,
flow_name: "my_flow".to_string(),
raw_sql: "sql".to_string(),
expire_after: Some(300),
@@ -213,12 +246,16 @@ mod tests {
let set = cache.get(1024).await.unwrap().unwrap();
assert_eq!(
set.as_ref().clone(),
HashMap::from_iter((1..=3).map(|i| { (i, Peer::empty(i),) }))
HashMap::from_iter(
(1..=3).map(|i| { (FlowIdent::new(1024, (i - 1) as u32), Peer::empty(i),) })
)
);
let set = cache.get(1025).await.unwrap().unwrap();
assert_eq!(
set.as_ref().clone(),
HashMap::from_iter((1..=3).map(|i| { (i, Peer::empty(i),) }))
HashMap::from_iter(
(1..=3).map(|i| { (FlowIdent::new(1024, (i - 1) as u32), Peer::empty(i),) })
)
);
let result = cache.get(1026).await.unwrap().unwrap();
assert_eq!(result.len(), 0);
@@ -230,8 +267,9 @@ mod tests {
let cache = CacheBuilder::new(128).build();
let cache = new_table_flownode_set_cache("test".to_string(), cache, mem_kv);
let ident = vec![CacheIdent::CreateFlow(CreateFlow {
flow_id: 2001,
source_table_ids: vec![1024, 1025],
flownodes: (1..=5).map(Peer::empty).collect(),
partition_to_peer_mapping: (1..=5).map(|i| (i as u32, Peer::empty(i + 1))).collect(),
})];
cache.invalidate(&ident).await.unwrap();
let set = cache.get(1024).await.unwrap().unwrap();
@@ -240,6 +278,54 @@ mod tests {
assert_eq!(set.len(), 5);
}
#[tokio::test]
async fn test_replace_flow() {
let mem_kv = Arc::new(MemoryKvBackend::default());
let cache = CacheBuilder::new(128).build();
let cache = new_table_flownode_set_cache("test".to_string(), cache, mem_kv);
let ident = vec![CacheIdent::CreateFlow(CreateFlow {
flow_id: 2001,
source_table_ids: vec![1024, 1025],
partition_to_peer_mapping: (1..=5).map(|i| (i as u32, Peer::empty(i + 1))).collect(),
})];
cache.invalidate(&ident).await.unwrap();
let set = cache.get(1024).await.unwrap().unwrap();
assert_eq!(set.len(), 5);
let set = cache.get(1025).await.unwrap().unwrap();
assert_eq!(set.len(), 5);
let drop_then_create_flow = vec![
CacheIdent::DropFlow(DropFlow {
flow_id: 2001,
source_table_ids: vec![1024, 1025],
flow_part2node_id: (1..=5).map(|i| (i as u32, i + 1)).collect(),
}),
CacheIdent::CreateFlow(CreateFlow {
flow_id: 2001,
source_table_ids: vec![1026, 1027],
partition_to_peer_mapping: (11..=15)
.map(|i| (i as u32, Peer::empty(i + 1)))
.collect(),
}),
CacheIdent::FlowId(2001),
];
cache.invalidate(&drop_then_create_flow).await.unwrap();
let set = cache.get(1024).await.unwrap().unwrap();
assert!(set.is_empty());
let expected = HashMap::from_iter(
(11..=15).map(|i| (FlowIdent::new(2001, i as u32), Peer::empty(i + 1))),
);
let set = cache.get(1026).await.unwrap().unwrap();
assert_eq!(set.as_ref().clone(), expected);
let set = cache.get(1027).await.unwrap().unwrap();
assert_eq!(set.as_ref().clone(), expected);
}
#[tokio::test]
async fn test_drop_flow() {
let mem_kv = Arc::new(MemoryKvBackend::default());
@@ -247,34 +333,57 @@ mod tests {
let cache = new_table_flownode_set_cache("test".to_string(), cache, mem_kv);
let ident = vec![
CacheIdent::CreateFlow(CreateFlow {
flow_id: 2001,
source_table_ids: vec![1024, 1025],
flownodes: (1..=5).map(Peer::empty).collect(),
partition_to_peer_mapping: (1..=5)
.map(|i| (i as u32, Peer::empty(i + 1)))
.collect(),
}),
CacheIdent::CreateFlow(CreateFlow {
flow_id: 2002,
source_table_ids: vec![1024, 1025],
flownodes: (11..=12).map(Peer::empty).collect(),
partition_to_peer_mapping: (11..=12)
.map(|i| (i as u32, Peer::empty(i + 1)))
.collect(),
}),
// same flownode that hold multiple flows
CacheIdent::CreateFlow(CreateFlow {
flow_id: 2003,
source_table_ids: vec![1024, 1025],
partition_to_peer_mapping: (1..=5)
.map(|i| (i as u32, Peer::empty(i + 1)))
.collect(),
}),
];
cache.invalidate(&ident).await.unwrap();
let set = cache.get(1024).await.unwrap().unwrap();
assert_eq!(set.len(), 7);
assert_eq!(set.len(), 12);
let set = cache.get(1025).await.unwrap().unwrap();
assert_eq!(set.len(), 7);
assert_eq!(set.len(), 12);
let ident = vec![CacheIdent::DropFlow(DropFlow {
flow_id: 2001,
source_table_ids: vec![1024, 1025],
flownode_ids: vec![1, 2, 3, 4, 5],
flow_part2node_id: (1..=5).map(|i| (i as u32, i + 1)).collect(),
})];
cache.invalidate(&ident).await.unwrap();
let set = cache.get(1024).await.unwrap().unwrap();
assert_eq!(
set.as_ref().clone(),
HashMap::from_iter((11..=12).map(|i| { (i, Peer::empty(i),) }))
HashMap::from_iter(
(11..=12)
.map(|i| (FlowIdent::new(2002, i as u32), Peer::empty(i + 1)))
.chain((1..=5).map(|i| (FlowIdent::new(2003, i as u32), Peer::empty(i + 1))))
)
);
let set = cache.get(1025).await.unwrap().unwrap();
assert_eq!(
set.as_ref().clone(),
HashMap::from_iter((11..=12).map(|i| { (i, Peer::empty(i),) }))
HashMap::from_iter(
(11..=12)
.map(|i| (FlowIdent::new(2002, i as u32), Peer::empty(i + 1)))
.chain((1..=5).map(|i| (FlowIdent::new(2003, i as u32), Peer::empty(i + 1))))
)
);
}
}

View File

@@ -16,9 +16,12 @@ use std::sync::Arc;
use crate::error::Result;
use crate::flow_name::FlowName;
use crate::instruction::CacheIdent;
use crate::instruction::{CacheIdent, DropFlow};
use crate::key::flow::flow_info::FlowInfoKey;
use crate::key::flow::flow_name::FlowNameKey;
use crate::key::flow::flow_route::FlowRouteKey;
use crate::key::flow::flownode_flow::FlownodeFlowKey;
use crate::key::flow::table_flow::TableFlowKey;
use crate::key::schema_name::SchemaNameKey;
use crate::key::table_info::TableInfoKey;
use crate::key::table_name::TableNameKey;
@@ -89,9 +92,40 @@ where
let key: SchemaNameKey = schema_name.into();
self.invalidate_key(&key.to_bytes()).await;
}
CacheIdent::CreateFlow(_) | CacheIdent::DropFlow(_) => {
CacheIdent::CreateFlow(_) => {
// Do nothing
}
CacheIdent::DropFlow(DropFlow {
flow_id,
source_table_ids,
flow_part2node_id,
}) => {
// invalidate flow route/flownode flow/table flow
let mut keys = Vec::with_capacity(
source_table_ids.len() * flow_part2node_id.len()
+ flow_part2node_id.len() * 2,
);
for table_id in source_table_ids {
for (partition_id, node_id) in flow_part2node_id {
let key =
TableFlowKey::new(*table_id, *node_id, *flow_id, *partition_id)
.to_bytes();
keys.push(key);
}
}
for (partition_id, node_id) in flow_part2node_id {
let key =
FlownodeFlowKey::new(*node_id, *flow_id, *partition_id).to_bytes();
keys.push(key);
let key = FlowRouteKey::new(*flow_id, *partition_id).to_bytes();
keys.push(key);
}
for key in keys {
self.invalidate_key(&key).await;
}
}
CacheIdent::FlowName(FlowName {
catalog_name,
flow_name,

View File

@@ -38,8 +38,8 @@ use table::metadata::TableId;
use crate::cache_invalidator::Context;
use crate::ddl::utils::{add_peer_context_if_needed, handle_retry_error};
use crate::ddl::DdlContext;
use crate::error::{self, Result};
use crate::instruction::{CacheIdent, CreateFlow};
use crate::error::{self, Result, UnexpectedSnafu};
use crate::instruction::{CacheIdent, CreateFlow, DropFlow};
use crate::key::flow::flow_info::FlowInfoValue;
use crate::key::flow::flow_route::FlowRouteValue;
use crate::key::table_name::TableNameKey;
@@ -70,6 +70,7 @@ impl CreateFlowProcedure {
query_context,
state: CreateFlowState::Prepare,
prev_flow_info_value: None,
did_replace: false,
flow_type: None,
},
}
@@ -171,7 +172,7 @@ impl CreateFlowProcedure {
}
self.data.state = CreateFlowState::CreateFlows;
// determine flow type
self.data.flow_type = Some(determine_flow_type(&self.data.task));
self.data.flow_type = Some(get_flow_type_from_options(&self.data.task)?);
Ok(Status::executing(true))
}
@@ -196,8 +197,8 @@ impl CreateFlowProcedure {
});
}
info!(
"Creating flow({:?}) on flownodes with peers={:?}",
self.data.flow_id, self.data.peers
"Creating flow({:?}, type={:?}) on flownodes with peers={:?}",
self.data.flow_id, self.data.flow_type, self.data.peers
);
join_all(create_flow)
.await
@@ -224,6 +225,7 @@ impl CreateFlowProcedure {
.update_flow_metadata(flow_id, prev_flow_value, &flow_info, flow_routes)
.await?;
info!("Replaced flow metadata for flow {flow_id}");
self.data.did_replace = true;
} else {
self.context
.flow_metadata_manager
@@ -240,22 +242,43 @@ impl CreateFlowProcedure {
debug_assert!(self.data.state == CreateFlowState::InvalidateFlowCache);
// Safety: The flow id must be allocated.
let flow_id = self.data.flow_id.unwrap();
let did_replace = self.data.did_replace;
let ctx = Context {
subject: Some("Invalidate flow cache by creating flow".to_string()),
};
let mut caches = vec![];
// if did replaced, invalidate the flow cache with drop the old flow
if did_replace {
let old_flow_info = self.data.prev_flow_info_value.as_ref().unwrap();
// only drop flow is needed, since flow name haven't changed, and flow id already invalidated below
caches.extend([CacheIdent::DropFlow(DropFlow {
flow_id,
source_table_ids: old_flow_info.source_table_ids.clone(),
flow_part2node_id: old_flow_info.flownode_ids().clone().into_iter().collect(),
})]);
}
let (_flow_info, flow_routes) = (&self.data).into();
let flow_part2peers = flow_routes
.into_iter()
.map(|(part_id, route)| (part_id, route.peer))
.collect();
caches.extend([
CacheIdent::CreateFlow(CreateFlow {
flow_id,
source_table_ids: self.data.source_table_ids.clone(),
partition_to_peer_mapping: flow_part2peers,
}),
CacheIdent::FlowId(flow_id),
]);
self.context
.cache_invalidator
.invalidate(
&ctx,
&[
CacheIdent::CreateFlow(CreateFlow {
source_table_ids: self.data.source_table_ids.clone(),
flownodes: self.data.peers.clone(),
}),
CacheIdent::FlowId(flow_id),
],
)
.invalidate(&ctx, &caches)
.await?;
Ok(Status::done_with_output(flow_id))
@@ -306,8 +329,20 @@ impl Procedure for CreateFlowProcedure {
}
}
pub fn determine_flow_type(_flow_task: &CreateFlowTask) -> FlowType {
FlowType::Batching
pub fn get_flow_type_from_options(flow_task: &CreateFlowTask) -> Result<FlowType> {
let flow_type = flow_task
.flow_options
.get(FlowType::FLOW_TYPE_KEY)
.map(|s| s.as_str());
match flow_type {
Some(FlowType::BATCHING) => Ok(FlowType::Batching),
Some(FlowType::STREAMING) => Ok(FlowType::Streaming),
Some(unknown) => UnexpectedSnafu {
err_msg: format!("Unknown flow type: {}", unknown),
}
.fail(),
None => Ok(FlowType::Batching),
}
}
/// The state of [CreateFlowProcedure].
@@ -365,6 +400,10 @@ pub struct CreateFlowData {
/// For verify if prev value is consistent when need to update flow metadata.
/// only set when `or_replace` is true.
pub(crate) prev_flow_info_value: Option<DeserializedValueWithBytes<FlowInfoValue>>,
/// Only set to true when replace actually happened.
/// This is used to determine whether to invalidate the cache.
#[serde(default)]
pub(crate) did_replace: bool,
pub(crate) flow_type: Option<FlowType>,
}
@@ -437,6 +476,7 @@ impl From<&CreateFlowData> for (FlowInfoValue, Vec<(FlowPartitionId, FlowRouteVa
sink_table_name,
flownode_ids,
catalog_name,
query_context: Some(value.query_context.clone()),
flow_name,
raw_sql: sql,
expire_after,

View File

@@ -13,6 +13,7 @@
// limitations under the License.
mod metadata;
use api::v1::flow::{flow_request, DropRequest, FlowRequest};
use async_trait::async_trait;
use common_catalog::format_full_flow_name;
@@ -153,6 +154,12 @@ impl DropFlowProcedure {
};
let flow_info_value = self.data.flow_info_value.as_ref().unwrap();
let flow_part2nodes = flow_info_value
.flownode_ids()
.clone()
.into_iter()
.collect::<Vec<_>>();
self.context
.cache_invalidator
.invalidate(
@@ -164,8 +171,9 @@ impl DropFlowProcedure {
flow_name: flow_info_value.flow_name.to_string(),
}),
CacheIdent::DropFlow(DropFlow {
flow_id,
source_table_ids: flow_info_value.source_table_ids.clone(),
flownode_ids: flow_info_value.flownode_ids.values().cloned().collect(),
flow_part2node_id: flow_part2nodes,
}),
],
)

View File

@@ -46,7 +46,7 @@ pub(crate) fn test_create_flow_task(
create_if_not_exists,
expire_after: Some(300),
comment: "".to_string(),
sql: "raw_sql".to_string(),
sql: "select 1".to_string(),
flow_options: Default::default(),
}
}

View File

@@ -401,6 +401,13 @@ pub enum Error {
location: Location,
},
#[snafu(display("Invalid flow request body: {:?}", body))]
InvalidFlowRequestBody {
body: Box<Option<api::v1::flow::flow_request::Body>>,
#[snafu(implicit)]
location: Location,
},
#[snafu(display("Failed to get kv cache, err: {}", err_msg))]
GetKvCache { err_msg: String },
@@ -507,11 +514,25 @@ pub enum Error {
},
#[snafu(display(
"Failed to build a Kafka partition client, topic: {}, partition: {}",
"Failed to get a Kafka partition client, topic: {}, partition: {}",
topic,
partition
))]
BuildKafkaPartitionClient {
KafkaPartitionClient {
topic: String,
partition: i32,
#[snafu(implicit)]
location: Location,
#[snafu(source)]
error: rskafka::client::error::Error,
},
#[snafu(display(
"Failed to get offset from Kafka, topic: {}, partition: {}",
topic,
partition
))]
KafkaGetOffset {
topic: String,
partition: i32,
#[snafu(implicit)]
@@ -784,74 +805,12 @@ pub enum Error {
source: common_procedure::error::Error,
},
#[snafu(display("Invalid file path: {}", file_path))]
InvalidFilePath {
#[snafu(implicit)]
location: Location,
file_path: String,
},
#[snafu(display("Failed to serialize flexbuffers"))]
SerializeFlexbuffers {
#[snafu(display("Failed to parse timezone"))]
InvalidTimeZone {
#[snafu(implicit)]
location: Location,
#[snafu(source)]
error: flexbuffers::SerializationError,
},
#[snafu(display("Failed to deserialize flexbuffers"))]
DeserializeFlexbuffers {
#[snafu(implicit)]
location: Location,
#[snafu(source)]
error: flexbuffers::DeserializationError,
},
#[snafu(display("Failed to read flexbuffers"))]
ReadFlexbuffers {
#[snafu(implicit)]
location: Location,
#[snafu(source)]
error: flexbuffers::ReaderError,
},
#[snafu(display("Invalid file name: {}", reason))]
InvalidFileName {
#[snafu(implicit)]
location: Location,
reason: String,
},
#[snafu(display("Invalid file extension: {}", reason))]
InvalidFileExtension {
#[snafu(implicit)]
location: Location,
reason: String,
},
#[snafu(display("Invalid file context: {}", reason))]
InvalidFileContext {
#[snafu(implicit)]
location: Location,
reason: String,
},
#[snafu(display("Failed to write object, file path: {}", file_path))]
WriteObject {
#[snafu(implicit)]
location: Location,
file_path: String,
#[snafu(source)]
error: object_store::Error,
},
#[snafu(display("Failed to read object, file path: {}", file_path))]
ReadObject {
#[snafu(implicit)]
location: Location,
file_path: String,
#[snafu(source)]
error: object_store::Error,
error: common_time::error::Error,
},
}
@@ -871,8 +830,6 @@ impl ErrorExt for Error {
| SerializeToJson { .. }
| DeserializeFromJson { .. } => StatusCode::Internal,
WriteObject { .. } | ReadObject { .. } => StatusCode::StorageUnavailable,
NoLeader { .. } => StatusCode::TableUnavailable,
ValueNotExist { .. } | ProcedurePoisonConflict { .. } => StatusCode::Unexpected,
@@ -900,7 +857,7 @@ impl ErrorExt for Error {
| EncodeWalOptions { .. }
| BuildKafkaClient { .. }
| BuildKafkaCtrlClient { .. }
| BuildKafkaPartitionClient { .. }
| KafkaPartitionClient { .. }
| ResolveKafkaEndpoint { .. }
| ProduceRecord { .. }
| CreateKafkaWalTopic { .. }
@@ -910,10 +867,7 @@ impl ErrorExt for Error {
| FromUtf8 { .. }
| MetadataCorruption { .. }
| ParseWalOptions { .. }
| ReadFlexbuffers { .. }
| SerializeFlexbuffers { .. }
| DeserializeFlexbuffers { .. }
| InvalidFileContext { .. } => StatusCode::Unexpected,
| KafkaGetOffset { .. } => StatusCode::Unexpected,
SendMessage { .. } | GetKvCache { .. } | CacheNotGet { .. } => StatusCode::Internal,
@@ -930,9 +884,8 @@ impl ErrorExt for Error {
| InvalidSetDatabaseOption { .. }
| InvalidUnsetDatabaseOption { .. }
| InvalidTopicNamePrefix { .. }
| InvalidFileExtension { .. }
| InvalidFileName { .. }
| InvalidFilePath { .. } => StatusCode::InvalidArguments,
| InvalidTimeZone { .. } => StatusCode::InvalidArguments,
InvalidFlowRequestBody { .. } => StatusCode::InvalidArguments,
FlowNotFound { .. } => StatusCode::FlowNotFound,
FlowRouteNotFound { .. } => StatusCode::Unexpected,

View File

@@ -24,7 +24,7 @@ use table::table_name::TableName;
use crate::flow_name::FlowName;
use crate::key::schema_name::SchemaName;
use crate::key::FlowId;
use crate::key::{FlowId, FlowPartitionId};
use crate::peer::Peer;
use crate::{DatanodeId, FlownodeId};
@@ -184,14 +184,19 @@ pub enum CacheIdent {
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub struct CreateFlow {
/// The unique identifier for the flow.
pub flow_id: FlowId,
pub source_table_ids: Vec<TableId>,
pub flownodes: Vec<Peer>,
/// Mapping of flow partition to peer information
pub partition_to_peer_mapping: Vec<(FlowPartitionId, Peer)>,
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub struct DropFlow {
pub flow_id: FlowId,
pub source_table_ids: Vec<TableId>,
pub flownode_ids: Vec<FlownodeId>,
/// Mapping of flow partition to flownode id
pub flow_part2node_id: Vec<(FlowPartitionId, FlownodeId)>,
}
/// Flushes a batch of regions.

View File

@@ -256,6 +256,11 @@ impl DatanodeTableManager {
})?
.and_then(|r| DatanodeTableValue::try_from_raw_value(&r.value))?
.region_info;
// If the region options are the same, we don't need to update it.
if region_info.region_options == new_region_options {
return Ok(Txn::new());
}
// substitute region options only.
region_info.region_options = new_region_options;

View File

@@ -45,7 +45,7 @@ use crate::kv_backend::KvBackendRef;
use crate::rpc::store::BatchDeleteRequest;
/// The key of `__flow/` scope.
#[derive(Debug, PartialEq)]
#[derive(Debug, Clone, PartialEq)]
pub struct FlowScoped<T> {
inner: T,
}
@@ -246,27 +246,32 @@ impl FlowMetadataManager {
new_flow_info: &FlowInfoValue,
flow_routes: Vec<(FlowPartitionId, FlowRouteValue)>,
) -> Result<()> {
let (create_flow_flow_name_txn, on_create_flow_flow_name_failure) =
let (update_flow_flow_name_txn, on_create_flow_flow_name_failure) =
self.flow_name_manager.build_update_txn(
&new_flow_info.catalog_name,
&new_flow_info.flow_name,
flow_id,
)?;
let (create_flow_txn, on_create_flow_failure) =
let (update_flow_txn, on_create_flow_failure) =
self.flow_info_manager
.build_update_txn(flow_id, current_flow_info, new_flow_info)?;
let create_flow_routes_txn = self
.flow_route_manager
.build_create_txn(flow_id, flow_routes.clone())?;
let create_flownode_flow_txn = self
.flownode_flow_manager
.build_create_txn(flow_id, new_flow_info.flownode_ids().clone());
let create_table_flow_txn = self.table_flow_manager.build_create_txn(
let update_flow_routes_txn = self.flow_route_manager.build_update_txn(
flow_id,
current_flow_info,
flow_routes.clone(),
)?;
let update_flownode_flow_txn = self.flownode_flow_manager.build_update_txn(
flow_id,
current_flow_info,
new_flow_info.flownode_ids().clone(),
);
let update_table_flow_txn = self.table_flow_manager.build_update_txn(
flow_id,
current_flow_info,
flow_routes
.into_iter()
.map(|(partition_id, route)| (partition_id, TableFlowValue { peer: route.peer }))
@@ -275,11 +280,11 @@ impl FlowMetadataManager {
)?;
let txn = Txn::merge_all(vec![
create_flow_flow_name_txn,
create_flow_txn,
create_flow_routes_txn,
create_flownode_flow_txn,
create_table_flow_txn,
update_flow_flow_name_txn,
update_flow_txn,
update_flow_routes_txn,
update_flownode_flow_txn,
update_table_flow_txn,
]);
info!(
"Creating flow {}.{}({}), with {} txn operations",
@@ -452,6 +457,7 @@ mod tests {
};
FlowInfoValue {
catalog_name: catalog_name.to_string(),
query_context: None,
flow_name: flow_name.to_string(),
source_table_ids,
sink_table_name,
@@ -625,6 +631,7 @@ mod tests {
};
let flow_value = FlowInfoValue {
catalog_name: "greptime".to_string(),
query_context: None,
flow_name: "flow".to_string(),
source_table_ids: vec![1024, 1025, 1026],
sink_table_name: another_sink_table_name,
@@ -781,6 +788,141 @@ mod tests {
}
}
#[tokio::test]
async fn test_update_flow_metadata_diff_flownode() {
let mem_kv = Arc::new(MemoryKvBackend::default());
let flow_metadata_manager = FlowMetadataManager::new(mem_kv.clone());
let flow_id = 10;
let flow_value = test_flow_info_value(
"flow",
[(0u32, 1u64), (1u32, 2u64)].into(),
vec![1024, 1025, 1026],
);
let flow_routes = vec![
(
0u32,
FlowRouteValue {
peer: Peer::empty(1),
},
),
(
1,
FlowRouteValue {
peer: Peer::empty(2),
},
),
];
flow_metadata_manager
.create_flow_metadata(flow_id, flow_value.clone(), flow_routes.clone())
.await
.unwrap();
let new_flow_value = {
let mut tmp = flow_value.clone();
tmp.raw_sql = "new".to_string();
// move to different flownodes
tmp.flownode_ids = [(0, 3u64), (1, 4u64)].into();
tmp
};
let new_flow_routes = vec![
(
0u32,
FlowRouteValue {
peer: Peer::empty(3),
},
),
(
1,
FlowRouteValue {
peer: Peer::empty(4),
},
),
];
// Update flow instead
flow_metadata_manager
.update_flow_metadata(
flow_id,
&DeserializedValueWithBytes::from_inner(flow_value.clone()),
&new_flow_value,
new_flow_routes.clone(),
)
.await
.unwrap();
let got = flow_metadata_manager
.flow_info_manager()
.get(flow_id)
.await
.unwrap()
.unwrap();
let routes = flow_metadata_manager
.flow_route_manager()
.routes(flow_id)
.await
.unwrap();
assert_eq!(
routes,
vec![
(
FlowRouteKey::new(flow_id, 0),
FlowRouteValue {
peer: Peer::empty(3),
},
),
(
FlowRouteKey::new(flow_id, 1),
FlowRouteValue {
peer: Peer::empty(4),
},
),
]
);
assert_eq!(got, new_flow_value);
let flows = flow_metadata_manager
.flownode_flow_manager()
.flows(1)
.try_collect::<Vec<_>>()
.await
.unwrap();
// should moved to different flownode
assert_eq!(flows, vec![]);
let flows = flow_metadata_manager
.flownode_flow_manager()
.flows(3)
.try_collect::<Vec<_>>()
.await
.unwrap();
assert_eq!(flows, vec![(flow_id, 0)]);
for table_id in [1024, 1025, 1026] {
let nodes = flow_metadata_manager
.table_flow_manager()
.flows(table_id)
.await
.unwrap();
assert_eq!(
nodes,
vec![
(
TableFlowKey::new(table_id, 3, flow_id, 0),
TableFlowValue {
peer: Peer::empty(3)
}
),
(
TableFlowKey::new(table_id, 4, flow_id, 1),
TableFlowValue {
peer: Peer::empty(4)
}
)
]
);
}
}
#[tokio::test]
async fn test_update_flow_metadata_flow_replace_diff_id_err() {
let mem_kv = Arc::new(MemoryKvBackend::default());
@@ -864,6 +1006,7 @@ mod tests {
};
let flow_value = FlowInfoValue {
catalog_name: "greptime".to_string(),
query_context: None,
flow_name: "flow".to_string(),
source_table_ids: vec![1024, 1025, 1026],
sink_table_name: another_sink_table_name,

View File

@@ -121,6 +121,13 @@ pub struct FlowInfoValue {
pub(crate) flownode_ids: BTreeMap<FlowPartitionId, FlownodeId>,
/// The catalog name.
pub(crate) catalog_name: String,
/// The query context used when create flow.
/// Although flow doesn't belong to any schema, this query_context is needed to remember
/// the query context when `create_flow` is executed
/// for recovering flow using the same sql&query_context after db restart.
/// if none, should use default query context
#[serde(default)]
pub(crate) query_context: Option<crate::rpc::ddl::QueryContext>,
/// The flow name.
pub(crate) flow_name: String,
/// The raw sql.
@@ -146,6 +153,15 @@ impl FlowInfoValue {
&self.flownode_ids
}
/// Insert a new flownode id for a partition.
pub fn insert_flownode_id(
&mut self,
partition: FlowPartitionId,
node: FlownodeId,
) -> Option<FlownodeId> {
self.flownode_ids.insert(partition, node)
}
/// Returns the `source_table`.
pub fn source_table_ids(&self) -> &[TableId] {
&self.source_table_ids
@@ -155,6 +171,10 @@ impl FlowInfoValue {
&self.catalog_name
}
pub fn query_context(&self) -> &Option<crate::rpc::ddl::QueryContext> {
&self.query_context
}
pub fn flow_name(&self) -> &String {
&self.flow_name
}
@@ -261,10 +281,11 @@ impl FlowInfoManager {
let raw_value = new_flow_value.try_as_raw_value()?;
let prev_value = current_flow_value.get_raw_bytes();
let txn = Txn::new()
.when(vec![
Compare::new(key.clone(), CompareOp::NotEqual, None),
Compare::new(key.clone(), CompareOp::Equal, Some(prev_value)),
])
.when(vec![Compare::new(
key.clone(),
CompareOp::Equal,
Some(prev_value),
)])
.and_then(vec![TxnOp::Put(key.clone(), raw_value)])
.or_else(vec![TxnOp::Get(key.clone())]);

View File

@@ -19,9 +19,12 @@ use serde::{Deserialize, Serialize};
use snafu::OptionExt;
use crate::error::{self, Result};
use crate::key::flow::flow_info::FlowInfoValue;
use crate::key::flow::{flownode_addr_helper, FlowScoped};
use crate::key::node_address::NodeAddressKey;
use crate::key::{BytesAdapter, FlowId, FlowPartitionId, MetadataKey, MetadataValue};
use crate::key::{
BytesAdapter, DeserializedValueWithBytes, FlowId, FlowPartitionId, MetadataKey, MetadataValue,
};
use crate::kv_backend::txn::{Txn, TxnOp};
use crate::kv_backend::KvBackendRef;
use crate::peer::Peer;
@@ -39,7 +42,7 @@ lazy_static! {
/// The key stores the route info of the flow.
///
/// The layout: `__flow/route/{flow_id}/{partition_id}`.
#[derive(Debug, PartialEq)]
#[derive(Debug, Clone, PartialEq)]
pub struct FlowRouteKey(FlowScoped<FlowRouteKeyInner>);
impl FlowRouteKey {
@@ -142,6 +145,12 @@ pub struct FlowRouteValue {
pub(crate) peer: Peer,
}
impl From<Peer> for FlowRouteValue {
fn from(peer: Peer) -> Self {
Self { peer }
}
}
impl FlowRouteValue {
/// Returns the `peer`.
pub fn peer(&self) -> &Peer {
@@ -204,6 +213,33 @@ impl FlowRouteManager {
Ok(Txn::new().and_then(txns))
}
/// Builds a update flow routes transaction.
///
/// Puts `__flow/route/{flow_id}/{partition_id}` keys.
/// Also removes `__flow/route/{flow_id}/{old_partition_id}` keys.
pub(crate) fn build_update_txn<I: IntoIterator<Item = (FlowPartitionId, FlowRouteValue)>>(
&self,
flow_id: FlowId,
current_flow_info: &DeserializedValueWithBytes<FlowInfoValue>,
flow_routes: I,
) -> Result<Txn> {
let del_txns = current_flow_info
.flownode_ids()
.iter()
.map(|(partition_id, _)| {
let key = FlowRouteKey::new(flow_id, *partition_id).to_bytes();
Ok(TxnOp::Delete(key))
});
let put_txns = flow_routes.into_iter().map(|(partition_id, route)| {
let key = FlowRouteKey::new(flow_id, partition_id).to_bytes();
Ok(TxnOp::Put(key, route.try_as_raw_value()?))
});
let txns = del_txns.chain(put_txns).collect::<Result<Vec<_>>>()?;
Ok(Txn::new().and_then(txns))
}
async fn remap_flow_route_addresses(
&self,
flow_routes: &mut [(FlowRouteKey, FlowRouteValue)],

View File

@@ -19,8 +19,9 @@ use regex::Regex;
use snafu::OptionExt;
use crate::error::{self, Result};
use crate::key::flow::flow_info::FlowInfoValue;
use crate::key::flow::FlowScoped;
use crate::key::{BytesAdapter, FlowId, FlowPartitionId, MetadataKey};
use crate::key::{BytesAdapter, DeserializedValueWithBytes, FlowId, FlowPartitionId, MetadataKey};
use crate::kv_backend::txn::{Txn, TxnOp};
use crate::kv_backend::KvBackendRef;
use crate::range_stream::{PaginationStream, DEFAULT_PAGE_SIZE};
@@ -165,6 +166,17 @@ impl FlownodeFlowManager {
Self { kv_backend }
}
/// Whether given flow exist on this flownode.
pub async fn exists(
&self,
flownode_id: FlownodeId,
flow_id: FlowId,
partition_id: FlowPartitionId,
) -> Result<bool> {
let key = FlownodeFlowKey::new(flownode_id, flow_id, partition_id).to_bytes();
Ok(self.kv_backend.get(&key).await?.is_some())
}
/// Retrieves all [FlowId] and [FlowPartitionId]s of the specified `flownode_id`.
pub fn flows(
&self,
@@ -202,6 +214,33 @@ impl FlownodeFlowManager {
Txn::new().and_then(txns)
}
/// Builds a update flownode flow transaction.
///
/// Puts `__flownode_flow/{flownode_id}/{flow_id}/{partition_id}` keys.
/// Remove the old `__flownode_flow/{old_flownode_id}/{flow_id}/{old_partition_id}` keys.
pub(crate) fn build_update_txn<I: IntoIterator<Item = (FlowPartitionId, FlownodeId)>>(
&self,
flow_id: FlowId,
current_flow_info: &DeserializedValueWithBytes<FlowInfoValue>,
flownode_ids: I,
) -> Txn {
let del_txns =
current_flow_info
.flownode_ids()
.iter()
.map(|(partition_id, flownode_id)| {
let key = FlownodeFlowKey::new(*flownode_id, flow_id, *partition_id).to_bytes();
TxnOp::Delete(key)
});
let put_txns = flownode_ids.into_iter().map(|(partition_id, flownode_id)| {
let key = FlownodeFlowKey::new(flownode_id, flow_id, partition_id).to_bytes();
TxnOp::Put(key, vec![])
});
let txns = del_txns.chain(put_txns).collect::<Vec<_>>();
Txn::new().and_then(txns)
}
}
#[cfg(test)]

View File

@@ -22,9 +22,12 @@ use snafu::OptionExt;
use table::metadata::TableId;
use crate::error::{self, Result};
use crate::key::flow::flow_info::FlowInfoValue;
use crate::key::flow::{flownode_addr_helper, FlowScoped};
use crate::key::node_address::NodeAddressKey;
use crate::key::{BytesAdapter, FlowId, FlowPartitionId, MetadataKey, MetadataValue};
use crate::key::{
BytesAdapter, DeserializedValueWithBytes, FlowId, FlowPartitionId, MetadataKey, MetadataValue,
};
use crate::kv_backend::txn::{Txn, TxnOp};
use crate::kv_backend::KvBackendRef;
use crate::peer::Peer;
@@ -215,7 +218,7 @@ impl TableFlowManager {
/// Builds a create table flow transaction.
///
/// Puts `__flow/source_table/{table_id}/{node_id}/{partition_id}` keys.
/// Puts `__flow/source_table/{table_id}/{node_id}/{flow_id}/{partition_id}` keys.
pub fn build_create_txn(
&self,
flow_id: FlowId,
@@ -239,6 +242,44 @@ impl TableFlowManager {
Ok(Txn::new().and_then(txns))
}
/// Builds a update table flow transaction.
///
/// Puts `__flow/source_table/{table_id}/{node_id}/{flow_id}/{partition_id}` keys,
/// Also remove previous
/// `__flow/source_table/{table_id}/{old_node_id}/{flow_id}/{partition_id}` keys.
pub fn build_update_txn(
&self,
flow_id: FlowId,
current_flow_info: &DeserializedValueWithBytes<FlowInfoValue>,
table_flow_values: Vec<(FlowPartitionId, TableFlowValue)>,
source_table_ids: &[TableId],
) -> Result<Txn> {
let mut txns = Vec::with_capacity(2 * source_table_ids.len() * table_flow_values.len());
// first remove the old keys
for (part_id, node_id) in current_flow_info.flownode_ids() {
for source_table_id in current_flow_info.source_table_ids() {
txns.push(TxnOp::Delete(
TableFlowKey::new(*source_table_id, *node_id, flow_id, *part_id).to_bytes(),
));
}
}
for (partition_id, table_flow_value) in table_flow_values {
let flownode_id = table_flow_value.peer.id;
let value = table_flow_value.try_as_raw_value()?;
for source_table_id in source_table_ids {
txns.push(TxnOp::Put(
TableFlowKey::new(*source_table_id, flownode_id, flow_id, partition_id)
.to_bytes(),
value.clone(),
));
}
}
Ok(Txn::new().and_then(txns))
}
async fn remap_table_flow_addresses(
&self,
table_flows: &mut [(TableFlowKey, TableFlowValue)],

View File

@@ -35,7 +35,7 @@ pub mod memory;
pub mod rds;
pub mod test;
pub mod txn;
pub mod util;
pub type KvBackendRef<E = Error> = Arc<dyn KvBackend<Error = E> + Send + Sync>;
#[async_trait]

View File

@@ -0,0 +1,85 @@
// Copyright 2023 Greptime Team
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
/// Removes sensitive information like passwords from connection strings.
///
/// This function sanitizes connection strings by removing credentials:
/// - For URL format (mysql://user:password@host:port/db): Removes everything before '@'
/// - For parameter format (host=localhost password=secret): Removes the password parameter
/// - For URL format without credentials (mysql://host:port/db): Removes the protocol prefix
///
/// # Arguments
///
/// * `conn_str` - The connection string to sanitize
///
/// # Returns
///
/// A sanitized version of the connection string with sensitive information removed
pub fn sanitize_connection_string(conn_str: &str) -> String {
// Case 1: URL format with credentials (mysql://user:password@host:port/db)
// Extract everything after the '@' symbol
if let Some(at_pos) = conn_str.find('@') {
return conn_str[at_pos + 1..].to_string();
}
// Case 2: Parameter format with password (host=localhost password=secret dbname=mydb)
// Filter out any parameter that starts with "password="
if conn_str.contains("password=") {
return conn_str
.split_whitespace()
.filter(|param| !param.starts_with("password="))
.collect::<Vec<_>>()
.join(" ");
}
// Case 3: URL format without credentials (mysql://host:port/db)
// Extract everything after the protocol prefix
if let Some(host_part) = conn_str.split("://").nth(1) {
return host_part.to_string();
}
// Case 4: Already sanitized or unknown format
// Return as is
conn_str.to_string()
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_sanitize_connection_string() {
// Test URL format with username/password
let conn_str = "mysql://user:password123@localhost:3306/db";
assert_eq!(sanitize_connection_string(conn_str), "localhost:3306/db");
// Test URL format without credentials
let conn_str = "mysql://localhost:3306/db";
assert_eq!(sanitize_connection_string(conn_str), "localhost:3306/db");
// Test parameter format with password
let conn_str = "host=localhost port=5432 user=postgres password=secret dbname=mydb";
assert_eq!(
sanitize_connection_string(conn_str),
"host=localhost port=5432 user=postgres dbname=mydb"
);
// Test parameter format without password
let conn_str = "host=localhost port=5432 user=postgres dbname=mydb";
assert_eq!(
sanitize_connection_string(conn_str),
"host=localhost port=5432 user=postgres dbname=mydb"
);
}
}

View File

@@ -43,7 +43,6 @@ pub mod region_keeper;
pub mod region_registry;
pub mod rpc;
pub mod sequence;
pub mod snapshot;
pub mod state_store;
#[cfg(any(test, feature = "testing"))]
pub mod test_util;

View File

@@ -113,8 +113,10 @@ impl LeaderRegionManifestInfo {
pub fn prunable_entry_id(&self) -> u64 {
match self {
LeaderRegionManifestInfo::Mito {
flushed_entry_id, ..
} => *flushed_entry_id,
flushed_entry_id,
topic_latest_entry_id,
..
} => (*flushed_entry_id).max(*topic_latest_entry_id),
LeaderRegionManifestInfo::Metric {
data_flushed_entry_id,
data_topic_latest_entry_id,

View File

@@ -35,17 +35,20 @@ use api::v1::{
};
use base64::engine::general_purpose;
use base64::Engine as _;
use common_time::DatabaseTimeToLive;
use common_time::{DatabaseTimeToLive, Timezone};
use prost::Message;
use serde::{Deserialize, Serialize};
use serde_with::{serde_as, DefaultOnNull};
use session::context::QueryContextRef;
use session::context::{QueryContextBuilder, QueryContextRef};
use snafu::{OptionExt, ResultExt};
use table::metadata::{RawTableInfo, TableId};
use table::table_name::TableName;
use table::table_reference::TableReference;
use crate::error::{self, InvalidSetDatabaseOptionSnafu, InvalidUnsetDatabaseOptionSnafu, Result};
use crate::error::{
self, InvalidSetDatabaseOptionSnafu, InvalidTimeZoneSnafu, InvalidUnsetDatabaseOptionSnafu,
Result,
};
use crate::key::FlowId;
/// DDL tasks
@@ -1202,7 +1205,7 @@ impl From<DropFlowTask> for PbDropFlowTask {
}
}
#[derive(Debug, Clone, Serialize, Deserialize)]
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub struct QueryContext {
current_catalog: String,
current_schema: String,
@@ -1223,6 +1226,19 @@ impl From<QueryContextRef> for QueryContext {
}
}
impl TryFrom<QueryContext> for session::context::QueryContext {
type Error = error::Error;
fn try_from(value: QueryContext) -> std::result::Result<Self, Self::Error> {
Ok(QueryContextBuilder::default()
.current_catalog(value.current_catalog)
.current_schema(value.current_schema)
.timezone(Timezone::from_tz_string(&value.timezone).context(InvalidTimeZoneSnafu)?)
.extensions(value.extensions)
.channel((value.channel as u32).into())
.build())
}
}
impl From<QueryContext> for PbQueryContext {
fn from(
QueryContext {

View File

@@ -1,365 +0,0 @@
// Copyright 2023 Greptime Team
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
pub mod file;
use std::fmt::{Display, Formatter};
use std::time::Instant;
use common_telemetry::info;
use file::{Metadata, MetadataContent};
use futures::TryStreamExt;
use object_store::ObjectStore;
use snafu::{OptionExt, ResultExt};
use strum::Display;
use crate::error::{
Error, InvalidFileExtensionSnafu, InvalidFileNameSnafu, InvalidFilePathSnafu, ReadObjectSnafu,
Result, WriteObjectSnafu,
};
use crate::kv_backend::KvBackendRef;
use crate::range_stream::{PaginationStream, DEFAULT_PAGE_SIZE};
use crate::rpc::store::{BatchPutRequest, RangeRequest};
use crate::rpc::KeyValue;
use crate::snapshot::file::{Document, KeyValue as FileKeyValue};
/// The format of the backup file.
#[derive(Debug, PartialEq, Eq, Display, Clone, Copy)]
pub enum FileFormat {
#[strum(serialize = "fb")]
FlexBuffers,
}
impl TryFrom<&str> for FileFormat {
type Error = String;
fn try_from(value: &str) -> std::result::Result<Self, Self::Error> {
match value.to_lowercase().as_str() {
"fb" => Ok(FileFormat::FlexBuffers),
_ => Err(format!("Invalid file format: {}", value)),
}
}
}
#[derive(Debug, PartialEq, Eq, Display)]
#[strum(serialize_all = "lowercase")]
pub enum DataType {
Metadata,
}
impl TryFrom<&str> for DataType {
type Error = String;
fn try_from(value: &str) -> std::result::Result<Self, Self::Error> {
match value.to_lowercase().as_str() {
"metadata" => Ok(DataType::Metadata),
_ => Err(format!("Invalid data type: {}", value)),
}
}
}
#[derive(Debug, PartialEq, Eq)]
pub struct FileExtension {
format: FileFormat,
data_type: DataType,
}
impl FileExtension {
pub fn new(format: FileFormat, data_type: DataType) -> Self {
Self { format, data_type }
}
}
impl Display for FileExtension {
fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
write!(f, "{}.{}", self.data_type, self.format)
}
}
impl TryFrom<&str> for FileExtension {
type Error = Error;
fn try_from(value: &str) -> Result<Self> {
let parts = value.split(".").collect::<Vec<&str>>();
if parts.len() != 2 {
return InvalidFileExtensionSnafu {
reason: format!(
"Extension should be in the format of <datatype>.<format>, got: {}",
value
),
}
.fail();
}
let data_type = DataType::try_from(parts[0])
.map_err(|e| InvalidFileExtensionSnafu { reason: e }.build())?;
let format = FileFormat::try_from(parts[1])
.map_err(|e| InvalidFileExtensionSnafu { reason: e }.build())?;
Ok(FileExtension { format, data_type })
}
}
#[derive(Debug, PartialEq, Eq)]
pub struct FileName {
name: String,
extension: FileExtension,
}
impl Display for FileName {
fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
write!(f, "{}.{}", self.name, self.extension)
}
}
impl TryFrom<&str> for FileName {
type Error = Error;
fn try_from(value: &str) -> Result<Self> {
let Some((name, extension)) = value.split_once(".") else {
return InvalidFileNameSnafu {
reason: format!(
"The file name should be in the format of <name>.<extension>, got: {}",
value
),
}
.fail();
};
let extension = FileExtension::try_from(extension)?;
Ok(Self {
name: name.to_string(),
extension,
})
}
}
impl FileName {
fn new(name: String, extension: FileExtension) -> Self {
Self { name, extension }
}
}
/// The manager of the metadata snapshot.
///
/// It manages the metadata snapshot, including dumping and restoring.
pub struct MetadataSnapshotManager {
kv_backend: KvBackendRef,
object_store: ObjectStore,
}
/// The maximum size of the request to put metadata, use 1MiB by default.
const MAX_REQUEST_SIZE: usize = 1024 * 1024;
impl MetadataSnapshotManager {
pub fn new(kv_backend: KvBackendRef, object_store: ObjectStore) -> Self {
Self {
kv_backend,
object_store,
}
}
/// Restores the metadata from the backup file to the metadata store.
pub async fn restore(&self, file_path: &str) -> Result<u64> {
let filename = FileName::try_from(
file_path
.rsplit("/")
.next()
.context(InvalidFilePathSnafu { file_path })?,
)?;
let data = self
.object_store
.read(file_path)
.await
.context(ReadObjectSnafu { file_path })?;
let document = Document::from_slice(&filename.extension.format, &data.to_bytes())?;
let metadata_content = document.into_metadata_content()?;
let mut req = BatchPutRequest::default();
let mut total_request_size = 0;
let mut count = 0;
let now = Instant::now();
for FileKeyValue { key, value } in metadata_content.into_iter() {
count += 1;
let key_size = key.len();
let value_size = value.len();
if total_request_size + key_size + value_size > MAX_REQUEST_SIZE {
self.kv_backend.batch_put(req).await?;
req = BatchPutRequest::default();
total_request_size = 0;
}
req.kvs.push(KeyValue { key, value });
total_request_size += key_size + value_size;
}
if !req.kvs.is_empty() {
self.kv_backend.batch_put(req).await?;
}
info!(
"Restored metadata from {} successfully, total {} key-value pairs, elapsed {:?}",
file_path,
count,
now.elapsed()
);
Ok(count)
}
/// Dumps the metadata to the backup file.
pub async fn dump(&self, path: &str, filename: &str) -> Result<(String, u64)> {
let format = FileFormat::FlexBuffers;
let filename = FileName::new(
filename.to_string(),
FileExtension {
format,
data_type: DataType::Metadata,
},
);
let file_path = format!("{}/{}", path.trim_end_matches('/'), filename);
let now = Instant::now();
let req = RangeRequest::new().with_range(vec![0], vec![0]);
let stream = PaginationStream::new(self.kv_backend.clone(), req, DEFAULT_PAGE_SIZE, |kv| {
Ok(FileKeyValue {
key: kv.key,
value: kv.value,
})
})
.into_stream();
let keyvalues = stream.try_collect::<Vec<_>>().await?;
let num_keyvalues = keyvalues.len();
let document = Document::new(
Metadata::new(),
file::Content::Metadata(MetadataContent::new(keyvalues)),
);
let bytes = document.to_bytes(&format)?;
let r = self
.object_store
.write(&file_path, bytes)
.await
.context(WriteObjectSnafu {
file_path: &file_path,
})?;
info!(
"Dumped metadata to {} successfully, total {} key-value pairs, file size {} bytes, elapsed {:?}",
file_path,
num_keyvalues,
r.content_length(),
now.elapsed()
);
Ok((filename.to_string(), num_keyvalues as u64))
}
}
#[cfg(test)]
mod tests {
use std::assert_matches::assert_matches;
use std::sync::Arc;
use common_test_util::temp_dir::{create_temp_dir, TempDir};
use object_store::services::Fs;
use super::*;
use crate::kv_backend::memory::MemoryKvBackend;
use crate::kv_backend::KvBackend;
use crate::rpc::store::PutRequest;
#[test]
fn test_file_name() {
let file_name = FileName::try_from("test.metadata.fb").unwrap();
assert_eq!(file_name.name, "test");
assert_eq!(file_name.extension.format, FileFormat::FlexBuffers);
assert_eq!(file_name.extension.data_type, DataType::Metadata);
assert_eq!(file_name.to_string(), "test.metadata.fb");
let invalid_file_name = FileName::try_from("test.metadata").unwrap_err();
assert_eq!(
invalid_file_name.to_string(),
"Invalid file extension: Extension should be in the format of <datatype>.<format>, got: metadata"
);
let invalid_file_extension = FileName::try_from("test.metadata.hello").unwrap_err();
assert_eq!(
invalid_file_extension.to_string(),
"Invalid file extension: Invalid file format: hello"
);
}
fn test_env(
prefix: &str,
) -> (
TempDir,
Arc<MemoryKvBackend<Error>>,
MetadataSnapshotManager,
) {
let temp_dir = create_temp_dir(prefix);
let kv_backend = Arc::new(MemoryKvBackend::default());
let temp_path = temp_dir.path();
let data_path = temp_path.join("data").as_path().display().to_string();
let builder = Fs::default().root(&data_path);
let object_store = ObjectStore::new(builder).unwrap().finish();
let manager = MetadataSnapshotManager::new(kv_backend.clone(), object_store);
(temp_dir, kv_backend, manager)
}
#[tokio::test]
async fn test_dump_and_restore() {
common_telemetry::init_default_ut_logging();
let (temp_dir, kv_backend, manager) = test_env("test_dump_and_restore");
let temp_path = temp_dir.path();
for i in 0..10 {
kv_backend
.put(
PutRequest::new()
.with_key(format!("test_{}", i).as_bytes().to_vec())
.with_value(format!("value_{}", i).as_bytes().to_vec()),
)
.await
.unwrap();
}
let dump_path = temp_path.join("snapshot");
manager
.dump(
&dump_path.as_path().display().to_string(),
"metadata_snapshot",
)
.await
.unwrap();
// Clean up the kv backend
kv_backend.clear();
let restore_path = dump_path
.join("metadata_snapshot.metadata.fb")
.as_path()
.display()
.to_string();
manager.restore(&restore_path).await.unwrap();
for i in 0..10 {
let key = format!("test_{}", i);
let value = kv_backend.get(key.as_bytes()).await.unwrap().unwrap();
assert_eq!(value.value, format!("value_{}", i).as_bytes());
}
}
#[tokio::test]
async fn test_restore_from_nonexistent_file() {
let (temp_dir, _kv_backend, manager) = test_env("test_restore_from_nonexistent_file");
let restore_path = temp_dir
.path()
.join("nonexistent.metadata.fb")
.as_path()
.display()
.to_string();
let err = manager.restore(&restore_path).await.unwrap_err();
assert_matches!(err, Error::ReadObject { .. })
}
}

View File

@@ -1,145 +0,0 @@
// Copyright 2023 Greptime Team
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
use common_time::util::current_time_millis;
use flexbuffers::{FlexbufferSerializer, Reader};
use serde::{Deserialize, Serialize};
use snafu::ResultExt;
use crate::error::{
DeserializeFlexbuffersSnafu, ReadFlexbuffersSnafu, Result, SerializeFlexbuffersSnafu,
};
use crate::snapshot::FileFormat;
/// The layout of the backup file.
#[derive(Debug, PartialEq, Serialize, Deserialize)]
pub(crate) struct Document {
metadata: Metadata,
content: Content,
}
impl Document {
/// Creates a new document.
pub fn new(metadata: Metadata, content: Content) -> Self {
Self { metadata, content }
}
fn serialize_to_flexbuffer(&self) -> Result<Vec<u8>> {
let mut builder = FlexbufferSerializer::new();
self.serialize(&mut builder)
.context(SerializeFlexbuffersSnafu)?;
Ok(builder.take_buffer())
}
/// Converts the [`Document`] to a bytes.
pub(crate) fn to_bytes(&self, format: &FileFormat) -> Result<Vec<u8>> {
match format {
FileFormat::FlexBuffers => self.serialize_to_flexbuffer(),
}
}
fn deserialize_from_flexbuffer(data: &[u8]) -> Result<Self> {
let reader = Reader::get_root(data).context(ReadFlexbuffersSnafu)?;
Document::deserialize(reader).context(DeserializeFlexbuffersSnafu)
}
/// Deserializes the [`Document`] from a bytes.
pub(crate) fn from_slice(format: &FileFormat, data: &[u8]) -> Result<Self> {
match format {
FileFormat::FlexBuffers => Self::deserialize_from_flexbuffer(data),
}
}
/// Converts the [`Document`] to a [`MetadataContent`].
pub(crate) fn into_metadata_content(self) -> Result<MetadataContent> {
match self.content {
Content::Metadata(metadata) => Ok(metadata),
}
}
}
/// The metadata of the backup file.
#[derive(Debug, PartialEq, Serialize, Deserialize)]
pub(crate) struct Metadata {
// UNIX_EPOCH in milliseconds.
created_timestamp_mills: i64,
}
impl Metadata {
/// Create a new metadata.
///
/// The `created_timestamp_mills` will be the current time in milliseconds.
pub fn new() -> Self {
Self {
created_timestamp_mills: current_time_millis(),
}
}
}
/// The content of the backup file.
#[derive(Debug, PartialEq, Serialize, Deserialize)]
pub(crate) enum Content {
Metadata(MetadataContent),
}
/// The content of the backup file.
#[derive(Debug, PartialEq, Serialize, Deserialize)]
pub(crate) struct MetadataContent {
values: Vec<KeyValue>,
}
impl MetadataContent {
/// Create a new metadata content.
pub fn new(values: impl IntoIterator<Item = KeyValue>) -> Self {
Self {
values: values.into_iter().collect(),
}
}
/// Returns an iterator over the key-value pairs.
pub fn into_iter(self) -> impl Iterator<Item = KeyValue> {
self.values.into_iter()
}
}
/// The key-value pair of the backup file.
#[derive(Debug, PartialEq, Serialize, Deserialize)]
pub(crate) struct KeyValue {
pub key: Vec<u8>,
pub value: Vec<u8>,
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_document() {
let document = Document::new(
Metadata::new(),
Content::Metadata(MetadataContent::new(vec![KeyValue {
key: b"key".to_vec(),
value: b"value".to_vec(),
}])),
);
let bytes = document.to_bytes(&FileFormat::FlexBuffers).unwrap();
let document_deserialized = Document::from_slice(&FileFormat::FlexBuffers, &bytes).unwrap();
assert_eq!(
document.metadata.created_timestamp_mills,
document_deserialized.metadata.created_timestamp_mills
);
assert_eq!(document.content, document_deserialized.content);
}
}

View File

@@ -20,6 +20,8 @@ use api::v1::region::{InsertRequests, RegionRequest};
pub use common_base::AffectedRows;
use common_query::request::QueryRequest;
use common_recordbatch::SendableRecordBatchStream;
use common_wal::config::kafka::common::{KafkaConnectionConfig, KafkaTopicConfig};
use common_wal::config::kafka::MetasrvKafkaConfig;
use crate::cache_invalidator::DummyCacheInvalidator;
use crate::ddl::flow_meta::FlowMetadataAllocator;
@@ -37,7 +39,8 @@ use crate::peer::{Peer, PeerLookupService};
use crate::region_keeper::MemoryRegionKeeper;
use crate::region_registry::LeaderRegionRegistry;
use crate::sequence::SequenceBuilder;
use crate::wal_options_allocator::WalOptionsAllocator;
use crate::wal_options_allocator::topic_pool::KafkaTopicPool;
use crate::wal_options_allocator::{build_kafka_topic_creator, WalOptionsAllocator};
use crate::{DatanodeId, FlownodeId};
#[async_trait::async_trait]
@@ -199,3 +202,34 @@ impl PeerLookupService for NoopPeerLookupService {
Ok(Some(Peer::empty(id)))
}
}
/// Create a kafka topic pool for testing.
pub async fn test_kafka_topic_pool(
broker_endpoints: Vec<String>,
num_topics: usize,
auto_create_topics: bool,
topic_name_prefix: Option<&str>,
) -> KafkaTopicPool {
let mut config = MetasrvKafkaConfig {
connection: KafkaConnectionConfig {
broker_endpoints,
..Default::default()
},
kafka_topic: KafkaTopicConfig {
num_topics,
..Default::default()
},
auto_create_topics,
..Default::default()
};
if let Some(prefix) = topic_name_prefix {
config.kafka_topic.topic_name_prefix = prefix.to_string();
}
let kv_backend = Arc::new(MemoryKvBackend::new()) as KvBackendRef;
let topic_creator = build_kafka_topic_creator(&config.connection, &config.kafka_topic)
.await
.unwrap();
KafkaTopicPool::new(&config, kv_backend, topic_creator)
}

View File

@@ -112,7 +112,9 @@ pub async fn build_wal_options_allocator(
NAME_PATTERN_REGEX.is_match(prefix),
InvalidTopicNamePrefixSnafu { prefix }
);
let topic_creator = build_kafka_topic_creator(kafka_config).await?;
let topic_creator =
build_kafka_topic_creator(&kafka_config.connection, &kafka_config.kafka_topic)
.await?;
let topic_pool = KafkaTopicPool::new(kafka_config, kv_backend, topic_creator);
Ok(WalOptionsAllocator::Kafka(topic_pool))
}
@@ -151,13 +153,16 @@ pub fn prepare_wal_options(
mod tests {
use std::assert_matches::assert_matches;
use common_wal::config::kafka::common::{KafkaConnectionConfig, KafkaTopicConfig};
use common_wal::config::kafka::common::KafkaTopicConfig;
use common_wal::config::kafka::MetasrvKafkaConfig;
use common_wal::test_util::run_test_with_kafka_wal;
use common_wal::maybe_skip_kafka_integration_test;
use common_wal::test_util::get_kafka_endpoints;
use super::*;
use crate::error::Error;
use crate::kv_backend::memory::MemoryKvBackend;
use crate::test_util::test_kafka_topic_pool;
use crate::wal_options_allocator::selector::RoundRobinTopicSelector;
// Tests that the wal options allocator could successfully allocate raft-engine wal options.
#[tokio::test]
@@ -197,55 +202,42 @@ mod tests {
assert_matches!(got, Error::InvalidTopicNamePrefix { .. });
}
// Tests that the wal options allocator could successfully allocate Kafka wal options.
#[tokio::test]
async fn test_allocator_with_kafka() {
run_test_with_kafka_wal(|broker_endpoints| {
Box::pin(async {
let topics = (0..256)
.map(|i| format!("test_allocator_with_kafka_{}_{}", i, uuid::Uuid::new_v4()))
.collect::<Vec<_>>();
// Creates a topic manager.
let kafka_topic = KafkaTopicConfig {
replication_factor: broker_endpoints.len() as i16,
..Default::default()
};
let config = MetasrvKafkaConfig {
connection: KafkaConnectionConfig {
broker_endpoints,
..Default::default()
},
kafka_topic,
..Default::default()
};
let kv_backend = Arc::new(MemoryKvBackend::new()) as KvBackendRef;
let topic_creator = build_kafka_topic_creator(&config).await.unwrap();
let mut topic_pool = KafkaTopicPool::new(&config, kv_backend, topic_creator);
topic_pool.topics.clone_from(&topics);
topic_pool.selector = Arc::new(selector::RoundRobinTopicSelector::default());
// Creates an options allocator.
let allocator = WalOptionsAllocator::Kafka(topic_pool);
allocator.start().await.unwrap();
let num_regions = 32;
let regions = (0..num_regions).collect::<Vec<_>>();
let got = allocate_region_wal_options(regions.clone(), &allocator, false).unwrap();
// Check the allocated wal options contain the expected topics.
let expected = (0..num_regions)
.map(|i| {
let options = WalOptions::Kafka(KafkaWalOptions {
topic: topics[i as usize].clone(),
});
(i, serde_json::to_string(&options).unwrap())
})
.collect::<HashMap<_, _>>();
assert_eq!(got, expected);
})
})
async fn test_allocator_with_kafka_allocate_wal_options() {
common_telemetry::init_default_ut_logging();
maybe_skip_kafka_integration_test!();
let num_topics = 5;
let mut topic_pool = test_kafka_topic_pool(
get_kafka_endpoints(),
num_topics,
true,
Some("test_allocator_with_kafka"),
)
.await;
topic_pool.selector = Arc::new(RoundRobinTopicSelector::default());
let topics = topic_pool.topics.clone();
// clean up the topics before test
let topic_creator = topic_pool.topic_creator();
topic_creator.delete_topics(&topics).await.unwrap();
// Creates an options allocator.
let allocator = WalOptionsAllocator::Kafka(topic_pool);
allocator.start().await.unwrap();
let num_regions = 3;
let regions = (0..num_regions).collect::<Vec<_>>();
let got = allocate_region_wal_options(regions.clone(), &allocator, false).unwrap();
// Check the allocated wal options contain the expected topics.
let expected = (0..num_regions)
.map(|i| {
let options = WalOptions::Kafka(KafkaWalOptions {
topic: topics[i as usize].clone(),
});
(i, serde_json::to_string(&options).unwrap())
})
.collect::<HashMap<_, _>>();
assert_eq!(got, expected);
}
#[tokio::test]

View File

@@ -12,20 +12,21 @@
// See the License for the specific language governing permissions and
// limitations under the License.
use common_telemetry::{error, info};
use common_wal::config::kafka::common::DEFAULT_BACKOFF_CONFIG;
use common_wal::config::kafka::MetasrvKafkaConfig;
use common_telemetry::{debug, error, info};
use common_wal::config::kafka::common::{
KafkaConnectionConfig, KafkaTopicConfig, DEFAULT_BACKOFF_CONFIG,
};
use rskafka::client::error::Error as RsKafkaError;
use rskafka::client::error::ProtocolError::TopicAlreadyExists;
use rskafka::client::partition::{Compression, UnknownTopicHandling};
use rskafka::client::partition::{Compression, OffsetAt, PartitionClient, UnknownTopicHandling};
use rskafka::client::{Client, ClientBuilder};
use rskafka::record::Record;
use snafu::ResultExt;
use crate::error::{
BuildKafkaClientSnafu, BuildKafkaCtrlClientSnafu, BuildKafkaPartitionClientSnafu,
CreateKafkaWalTopicSnafu, ProduceRecordSnafu, ResolveKafkaEndpointSnafu, Result,
TlsConfigSnafu,
BuildKafkaClientSnafu, BuildKafkaCtrlClientSnafu, CreateKafkaWalTopicSnafu,
KafkaGetOffsetSnafu, KafkaPartitionClientSnafu, ProduceRecordSnafu, ResolveKafkaEndpointSnafu,
Result, TlsConfigSnafu,
};
// Each topic only has one partition for now.
@@ -70,21 +71,47 @@ impl KafkaTopicCreator {
info!("The topic {} already exists", topic);
Ok(())
} else {
error!("Failed to create a topic {}, error {:?}", topic, e);
error!(e; "Failed to create a topic {}", topic);
Err(e).context(CreateKafkaWalTopicSnafu)
}
}
}
}
async fn append_noop_record(&self, topic: &String, client: &Client) -> Result<()> {
let partition_client = client
async fn prepare_topic(&self, topic: &String) -> Result<()> {
let partition_client = self.partition_client(topic).await?;
self.append_noop_record(topic, &partition_client).await?;
Ok(())
}
/// Creates a [PartitionClient] for the given topic.
async fn partition_client(&self, topic: &str) -> Result<PartitionClient> {
self.client
.partition_client(topic, DEFAULT_PARTITION, UnknownTopicHandling::Retry)
.await
.context(BuildKafkaPartitionClientSnafu {
.context(KafkaPartitionClientSnafu {
topic,
partition: DEFAULT_PARTITION,
})
}
/// Appends a noop record to the topic.
/// It only appends a noop record if the topic is empty.
async fn append_noop_record(
&self,
topic: &String,
partition_client: &PartitionClient,
) -> Result<()> {
let end_offset = partition_client
.get_offset(OffsetAt::Latest)
.await
.context(KafkaGetOffsetSnafu {
topic: topic.to_string(),
partition: DEFAULT_PARTITION,
})?;
if end_offset > 0 {
return Ok(());
}
partition_client
.produce(
@@ -98,22 +125,28 @@ impl KafkaTopicCreator {
)
.await
.context(ProduceRecordSnafu { topic })?;
debug!("Appended a noop record to topic {}", topic);
Ok(())
}
/// Creates topics in Kafka.
pub async fn create_topics(&self, topics: &[String]) -> Result<()> {
let tasks = topics
.iter()
.map(|topic| async { self.create_topic(topic, &self.client).await })
.collect::<Vec<_>>();
futures::future::try_join_all(tasks).await.map(|_| ())
}
/// Prepares topics in Kafka.
/// 1. Creates missing topics.
/// 2. Appends a noop record to each topic.
pub async fn prepare_topics(&self, topics: &[&String]) -> Result<()> {
///
/// It appends a noop record to each topic if the topic is empty.
pub async fn prepare_topics(&self, topics: &[String]) -> Result<()> {
// Try to create missing topics.
let tasks = topics
.iter()
.map(|topic| async {
self.create_topic(topic, &self.client).await?;
self.append_noop_record(topic, &self.client).await?;
Ok(())
})
.map(|topic| async { self.prepare_topic(topic).await })
.collect::<Vec<_>>();
futures::future::try_join_all(tasks).await.map(|_| ())
}
@@ -129,34 +162,244 @@ impl KafkaTopicCreator {
}
}
#[cfg(test)]
impl KafkaTopicCreator {
pub async fn delete_topics(&self, topics: &[String]) -> Result<()> {
let tasks = topics
.iter()
.map(|topic| async { self.delete_topic(topic, &self.client).await })
.collect::<Vec<_>>();
futures::future::try_join_all(tasks).await.map(|_| ())
}
async fn delete_topic(&self, topic: &String, client: &Client) -> Result<()> {
let controller = client
.controller_client()
.context(BuildKafkaCtrlClientSnafu)?;
match controller.delete_topic(topic, 10).await {
Ok(_) => {
info!("Successfully deleted topic {}", topic);
Ok(())
}
Err(e) => {
if Self::is_unknown_topic_err(&e) {
info!("The topic {} does not exist", topic);
Ok(())
} else {
panic!("Failed to delete a topic {}, error: {}", topic, e);
}
}
}
}
fn is_unknown_topic_err(e: &RsKafkaError) -> bool {
matches!(
e,
&RsKafkaError::ServerError {
protocol_error: rskafka::client::error::ProtocolError::UnknownTopicOrPartition,
..
}
)
}
pub async fn get_partition_client(&self, topic: &str) -> PartitionClient {
self.partition_client(topic).await.unwrap()
}
}
/// Builds a kafka [Client](rskafka::client::Client).
pub async fn build_kafka_client(config: &MetasrvKafkaConfig) -> Result<Client> {
pub async fn build_kafka_client(connection: &KafkaConnectionConfig) -> Result<Client> {
// Builds an kafka controller client for creating topics.
let broker_endpoints = common_wal::resolve_to_ipv4(&config.connection.broker_endpoints)
let broker_endpoints = common_wal::resolve_to_ipv4(&connection.broker_endpoints)
.await
.context(ResolveKafkaEndpointSnafu)?;
let mut builder = ClientBuilder::new(broker_endpoints).backoff_config(DEFAULT_BACKOFF_CONFIG);
if let Some(sasl) = &config.connection.sasl {
if let Some(sasl) = &connection.sasl {
builder = builder.sasl_config(sasl.config.clone().into_sasl_config());
};
if let Some(tls) = &config.connection.tls {
if let Some(tls) = &connection.tls {
builder = builder.tls_config(tls.to_tls_config().await.context(TlsConfigSnafu)?)
};
builder
.build()
.await
.with_context(|_| BuildKafkaClientSnafu {
broker_endpoints: config.connection.broker_endpoints.clone(),
broker_endpoints: connection.broker_endpoints.clone(),
})
}
/// Builds a [KafkaTopicCreator].
pub async fn build_kafka_topic_creator(config: &MetasrvKafkaConfig) -> Result<KafkaTopicCreator> {
let client = build_kafka_client(config).await?;
pub async fn build_kafka_topic_creator(
connection: &KafkaConnectionConfig,
kafka_topic: &KafkaTopicConfig,
) -> Result<KafkaTopicCreator> {
let client = build_kafka_client(connection).await?;
Ok(KafkaTopicCreator {
client,
num_partitions: config.kafka_topic.num_partitions,
replication_factor: config.kafka_topic.replication_factor,
create_topic_timeout: config.kafka_topic.create_topic_timeout.as_millis() as i32,
num_partitions: kafka_topic.num_partitions,
replication_factor: kafka_topic.replication_factor,
create_topic_timeout: kafka_topic.create_topic_timeout.as_millis() as i32,
})
}
#[cfg(test)]
mod tests {
use common_wal::config::kafka::common::{KafkaConnectionConfig, KafkaTopicConfig};
use common_wal::maybe_skip_kafka_integration_test;
use common_wal::test_util::get_kafka_endpoints;
use super::*;
async fn test_topic_creator(broker_endpoints: Vec<String>) -> KafkaTopicCreator {
let connection = KafkaConnectionConfig {
broker_endpoints,
..Default::default()
};
let kafka_topic = KafkaTopicConfig::default();
build_kafka_topic_creator(&connection, &kafka_topic)
.await
.unwrap()
}
async fn append_records(partition_client: &PartitionClient, num_records: usize) -> Result<()> {
for i in 0..num_records {
partition_client
.produce(
vec![Record {
key: Some(b"test".to_vec()),
value: Some(format!("test {}", i).as_bytes().to_vec()),
timestamp: chrono::Utc::now(),
headers: Default::default(),
}],
Compression::Lz4,
)
.await
.unwrap();
}
Ok(())
}
#[tokio::test]
async fn test_append_noop_record_to_empty_topic() {
common_telemetry::init_default_ut_logging();
maybe_skip_kafka_integration_test!();
let prefix = "append_noop_record_to_empty_topic";
let creator = test_topic_creator(get_kafka_endpoints()).await;
let topic = format!("{}{}", prefix, "0");
// Clean up the topics before test
creator.delete_topics(&[topic.to_string()]).await.unwrap();
creator.create_topics(&[topic.to_string()]).await.unwrap();
let partition_client = creator.partition_client(&topic).await.unwrap();
let end_offset = partition_client.get_offset(OffsetAt::Latest).await.unwrap();
assert_eq!(end_offset, 0);
// The topic is not empty, so no noop record is appended.
creator
.append_noop_record(&topic, &partition_client)
.await
.unwrap();
let end_offset = partition_client.get_offset(OffsetAt::Latest).await.unwrap();
assert_eq!(end_offset, 1);
}
#[tokio::test]
async fn test_append_noop_record_to_non_empty_topic() {
common_telemetry::init_default_ut_logging();
maybe_skip_kafka_integration_test!();
let prefix = "append_noop_record_to_non_empty_topic";
let creator = test_topic_creator(get_kafka_endpoints()).await;
let topic = format!("{}{}", prefix, "0");
// Clean up the topics before test
creator.delete_topics(&[topic.to_string()]).await.unwrap();
creator.create_topics(&[topic.to_string()]).await.unwrap();
let partition_client = creator.partition_client(&topic).await.unwrap();
append_records(&partition_client, 2).await.unwrap();
let end_offset = partition_client.get_offset(OffsetAt::Latest).await.unwrap();
assert_eq!(end_offset, 2);
// The topic is not empty, so no noop record is appended.
creator
.append_noop_record(&topic, &partition_client)
.await
.unwrap();
let end_offset = partition_client.get_offset(OffsetAt::Latest).await.unwrap();
assert_eq!(end_offset, 2);
}
#[tokio::test]
async fn test_create_topic() {
common_telemetry::init_default_ut_logging();
maybe_skip_kafka_integration_test!();
let prefix = "create_topic";
let creator = test_topic_creator(get_kafka_endpoints()).await;
let topic = format!("{}{}", prefix, "0");
// Clean up the topics before test
creator.delete_topics(&[topic.to_string()]).await.unwrap();
creator.create_topics(&[topic.to_string()]).await.unwrap();
// Should be ok
creator.create_topics(&[topic.to_string()]).await.unwrap();
let partition_client = creator.partition_client(&topic).await.unwrap();
let end_offset = partition_client.get_offset(OffsetAt::Latest).await.unwrap();
assert_eq!(end_offset, 0);
}
#[tokio::test]
async fn test_prepare_topic() {
common_telemetry::init_default_ut_logging();
maybe_skip_kafka_integration_test!();
let prefix = "prepare_topic";
let creator = test_topic_creator(get_kafka_endpoints()).await;
let topic = format!("{}{}", prefix, "0");
// Clean up the topics before test
creator.delete_topics(&[topic.to_string()]).await.unwrap();
creator.create_topics(&[topic.to_string()]).await.unwrap();
creator.prepare_topic(&topic).await.unwrap();
let partition_client = creator.partition_client(&topic).await.unwrap();
let start_offset = partition_client
.get_offset(OffsetAt::Earliest)
.await
.unwrap();
assert_eq!(start_offset, 0);
let end_offset = partition_client.get_offset(OffsetAt::Latest).await.unwrap();
assert_eq!(end_offset, 1);
}
#[tokio::test]
async fn test_prepare_topic_with_stale_records_without_pruning() {
common_telemetry::init_default_ut_logging();
maybe_skip_kafka_integration_test!();
let prefix = "prepare_topic_with_stale_records_without_pruning";
let creator = test_topic_creator(get_kafka_endpoints()).await;
let topic = format!("{}{}", prefix, "0");
// Clean up the topics before test
creator.delete_topics(&[topic.to_string()]).await.unwrap();
creator.create_topics(&[topic.to_string()]).await.unwrap();
let partition_client = creator.partition_client(&topic).await.unwrap();
append_records(&partition_client, 10).await.unwrap();
creator.prepare_topic(&topic).await.unwrap();
let end_offset = partition_client.get_offset(OffsetAt::Latest).await.unwrap();
assert_eq!(end_offset, 10);
let start_offset = partition_client
.get_offset(OffsetAt::Earliest)
.await
.unwrap();
assert_eq!(start_offset, 0);
}
}

View File

@@ -40,24 +40,21 @@ impl KafkaTopicManager {
Ok(topics)
}
/// Restores topics from the key-value backend. and returns the topics that are not stored in kvbackend.
pub async fn get_topics_to_create<'a>(
&self,
all_topics: &'a [String],
) -> Result<Vec<&'a String>> {
/// Returns the topics that are not prepared.
pub async fn unprepare_topics(&self, all_topics: &[String]) -> Result<Vec<String>> {
let existing_topics = self.restore_topics().await?;
let existing_topic_set = existing_topics.iter().collect::<HashSet<_>>();
let mut topics_to_create = Vec::with_capacity(all_topics.len());
for topic in all_topics {
if !existing_topic_set.contains(topic) {
topics_to_create.push(topic);
topics_to_create.push(topic.to_string());
}
}
Ok(topics_to_create)
}
/// Persists topics into the key-value backend.
pub async fn persist_topics(&self, topics: &[String]) -> Result<()> {
/// Persists prepared topics into the key-value backend.
pub async fn persist_prepared_topics(&self, topics: &[String]) -> Result<()> {
self.topic_name_manager
.batch_put(
topics
@@ -70,6 +67,14 @@ impl KafkaTopicManager {
}
}
#[cfg(test)]
impl KafkaTopicManager {
/// Lists all topics in the key-value backend.
pub async fn list_topics(&self) -> Result<Vec<String>> {
self.topic_name_manager.range().await
}
}
#[cfg(test)]
mod tests {
use std::sync::Arc;
@@ -90,11 +95,11 @@ mod tests {
// No legacy topics.
let mut topics_to_be_created = topic_kvbackend_manager
.get_topics_to_create(&all_topics)
.unprepare_topics(&all_topics)
.await
.unwrap();
topics_to_be_created.sort();
let mut expected = all_topics.iter().collect::<Vec<_>>();
let mut expected = all_topics.clone();
expected.sort();
assert_eq!(expected, topics_to_be_created);
@@ -109,7 +114,7 @@ mod tests {
assert!(res.prev_kv.is_none());
let topics_to_be_created = topic_kvbackend_manager
.get_topics_to_create(&all_topics)
.unprepare_topics(&all_topics)
.await
.unwrap();
assert!(topics_to_be_created.is_empty());
@@ -144,21 +149,21 @@ mod tests {
let topic_kvbackend_manager = KafkaTopicManager::new(kv_backend);
let mut topics_to_be_created = topic_kvbackend_manager
.get_topics_to_create(&all_topics)
.unprepare_topics(&all_topics)
.await
.unwrap();
topics_to_be_created.sort();
let mut expected = all_topics.iter().collect::<Vec<_>>();
let mut expected = all_topics.clone();
expected.sort();
assert_eq!(expected, topics_to_be_created);
// Persists topics to kv backend.
topic_kvbackend_manager
.persist_topics(&all_topics)
.persist_prepared_topics(&all_topics)
.await
.unwrap();
let topics_to_be_created = topic_kvbackend_manager
.get_topics_to_create(&all_topics)
.unprepare_topics(&all_topics)
.await
.unwrap();
assert!(topics_to_be_created.is_empty());

View File

@@ -15,6 +15,7 @@
use std::fmt::{self, Formatter};
use std::sync::Arc;
use common_telemetry::info;
use common_wal::config::kafka::MetasrvKafkaConfig;
use common_wal::TopicSelectorType;
use snafu::ensure;
@@ -77,27 +78,35 @@ impl KafkaTopicPool {
}
/// Tries to activate the topic manager when metasrv becomes the leader.
///
/// First tries to restore persisted topics from the kv backend.
/// If not enough topics retrieved, it will try to contact the Kafka cluster and request creating more topics.
/// If there are unprepared topics (topics that exist in the configuration but not in the kv backend),
/// it will create these topics in Kafka if `auto_create_topics` is enabled.
///
/// Then it prepares all unprepared topics by appending a noop record if the topic is empty,
/// and persists them in the kv backend for future use.
pub async fn activate(&self) -> Result<()> {
if !self.auto_create_topics {
return Ok(());
}
let num_topics = self.topics.len();
ensure!(num_topics > 0, InvalidNumTopicsSnafu { num_topics });
let topics_to_be_created = self
.topic_manager
.get_topics_to_create(&self.topics)
.await?;
let unprepared_topics = self.topic_manager.unprepare_topics(&self.topics).await?;
if !topics_to_be_created.is_empty() {
if !unprepared_topics.is_empty() {
if self.auto_create_topics {
info!("Creating {} topics", unprepared_topics.len());
self.topic_creator.create_topics(&unprepared_topics).await?;
} else {
info!("Auto create topics is disabled, skipping topic creation.");
}
self.topic_creator
.prepare_topics(&topics_to_be_created)
.prepare_topics(&unprepared_topics)
.await?;
self.topic_manager
.persist_prepared_topics(&unprepared_topics)
.await?;
self.topic_manager.persist_topics(&self.topics).await?;
}
info!("Activated topic pool with {} topics", self.topics.len());
Ok(())
}
@@ -114,77 +123,147 @@ impl KafkaTopicPool {
}
}
#[cfg(test)]
impl KafkaTopicPool {
pub(crate) fn topic_manager(&self) -> &KafkaTopicManager {
&self.topic_manager
}
pub(crate) fn topic_creator(&self) -> &KafkaTopicCreator {
&self.topic_creator
}
}
#[cfg(test)]
mod tests {
use common_wal::config::kafka::common::{KafkaConnectionConfig, KafkaTopicConfig};
use common_wal::test_util::run_test_with_kafka_wal;
use std::assert_matches::assert_matches;
use common_wal::maybe_skip_kafka_integration_test;
use common_wal::test_util::get_kafka_endpoints;
use super::*;
use crate::kv_backend::memory::MemoryKvBackend;
use crate::wal_options_allocator::topic_creator::build_kafka_topic_creator;
use crate::error::Error;
use crate::test_util::test_kafka_topic_pool;
use crate::wal_options_allocator::selector::RoundRobinTopicSelector;
#[tokio::test]
async fn test_pool_invalid_number_topics_err() {
common_telemetry::init_default_ut_logging();
maybe_skip_kafka_integration_test!();
let endpoints = get_kafka_endpoints();
let pool = test_kafka_topic_pool(endpoints.clone(), 0, false, None).await;
let err = pool.activate().await.unwrap_err();
assert_matches!(err, Error::InvalidNumTopics { .. });
let pool = test_kafka_topic_pool(endpoints, 0, true, None).await;
let err = pool.activate().await.unwrap_err();
assert_matches!(err, Error::InvalidNumTopics { .. });
}
#[tokio::test]
async fn test_pool_activate_unknown_topics_err() {
common_telemetry::init_default_ut_logging();
maybe_skip_kafka_integration_test!();
let pool =
test_kafka_topic_pool(get_kafka_endpoints(), 1, false, Some("unknown_topic")).await;
let err = pool.activate().await.unwrap_err();
assert_matches!(err, Error::KafkaPartitionClient { .. });
}
#[tokio::test]
async fn test_pool_activate() {
common_telemetry::init_default_ut_logging();
maybe_skip_kafka_integration_test!();
let pool =
test_kafka_topic_pool(get_kafka_endpoints(), 2, true, Some("pool_activate")).await;
// clean up the topics before test
let topic_creator = pool.topic_creator();
topic_creator.delete_topics(&pool.topics).await.unwrap();
let topic_manager = pool.topic_manager();
pool.activate().await.unwrap();
let topics = topic_manager.list_topics().await.unwrap();
assert_eq!(topics.len(), 2);
}
#[tokio::test]
async fn test_pool_activate_with_existing_topics() {
common_telemetry::init_default_ut_logging();
maybe_skip_kafka_integration_test!();
let prefix = "pool_activate_with_existing_topics";
let pool = test_kafka_topic_pool(get_kafka_endpoints(), 2, true, Some(prefix)).await;
let topic_creator = pool.topic_creator();
topic_creator.delete_topics(&pool.topics).await.unwrap();
let topic_manager = pool.topic_manager();
// persists one topic info, then pool.activate() will create new topics that not persisted.
topic_manager
.persist_prepared_topics(&pool.topics[0..1])
.await
.unwrap();
pool.activate().await.unwrap();
let topics = topic_manager.list_topics().await.unwrap();
assert_eq!(topics.len(), 2);
let client = pool.topic_creator().client();
let topics = client
.list_topics()
.await
.unwrap()
.into_iter()
.filter(|t| t.name.starts_with(prefix))
.collect::<Vec<_>>();
assert_eq!(topics.len(), 1);
}
/// Tests that the topic manager could allocate topics correctly.
#[tokio::test]
async fn test_alloc_topics() {
run_test_with_kafka_wal(|broker_endpoints| {
Box::pin(async {
// Constructs topics that should be created.
let topics = (0..256)
.map(|i| format!("test_alloc_topics_{}_{}", i, uuid::Uuid::new_v4()))
.collect::<Vec<_>>();
// Creates a topic manager.
let kafka_topic = KafkaTopicConfig {
replication_factor: broker_endpoints.len() as i16,
..Default::default()
};
let config = MetasrvKafkaConfig {
connection: KafkaConnectionConfig {
broker_endpoints,
..Default::default()
},
kafka_topic,
..Default::default()
};
let kv_backend = Arc::new(MemoryKvBackend::new()) as KvBackendRef;
let topic_creator = build_kafka_topic_creator(&config).await.unwrap();
let mut topic_pool = KafkaTopicPool::new(&config, kv_backend, topic_creator);
// Replaces the default topic pool with the constructed topics.
topic_pool.topics.clone_from(&topics);
// Replaces the default selector with a round-robin selector without shuffled.
topic_pool.selector = Arc::new(RoundRobinTopicSelector::default());
topic_pool.activate().await.unwrap();
// Selects exactly the number of `num_topics` topics one by one.
let got = (0..topics.len())
.map(|_| topic_pool.select().unwrap())
.cloned()
.collect::<Vec<_>>();
assert_eq!(got, topics);
// Selects exactly the number of `num_topics` topics in a batching manner.
let got = topic_pool
.select_batch(topics.len())
.unwrap()
.into_iter()
.map(ToString::to_string)
.collect::<Vec<_>>();
assert_eq!(got, topics);
// Selects more than the number of `num_topics` topics.
let got = topic_pool
.select_batch(2 * topics.len())
.unwrap()
.into_iter()
.map(ToString::to_string)
.collect::<Vec<_>>();
let expected = vec![topics.clone(); 2]
.into_iter()
.flatten()
.collect::<Vec<_>>();
assert_eq!(got, expected);
})
})
common_telemetry::init_default_ut_logging();
maybe_skip_kafka_integration_test!();
let num_topics = 5;
let mut topic_pool = test_kafka_topic_pool(
get_kafka_endpoints(),
num_topics,
true,
Some("test_allocator_with_kafka"),
)
.await;
topic_pool.selector = Arc::new(RoundRobinTopicSelector::default());
let topics = topic_pool.topics.clone();
// clean up the topics before test
let topic_creator = topic_pool.topic_creator();
topic_creator.delete_topics(&topics).await.unwrap();
// Selects exactly the number of `num_topics` topics one by one.
let got = (0..topics.len())
.map(|_| topic_pool.select().unwrap())
.cloned()
.collect::<Vec<_>>();
assert_eq!(got, topics);
// Selects exactly the number of `num_topics` topics in a batching manner.
let got = topic_pool
.select_batch(topics.len())
.unwrap()
.into_iter()
.map(ToString::to_string)
.collect::<Vec<_>>();
assert_eq!(got, topics);
// Selects more than the number of `num_topics` topics.
let got = topic_pool
.select_batch(2 * topics.len())
.unwrap()
.into_iter()
.map(ToString::to_string)
.collect::<Vec<_>>();
let expected = vec![topics.clone(); 2]
.into_iter()
.flatten()
.collect::<Vec<_>>();
assert_eq!(got, expected);
}
}

View File

@@ -162,13 +162,6 @@ pub enum Error {
location: Location,
},
#[snafu(display("Failed to do metadata snapshot"))]
MetadataSnapshot {
source: BoxedError,
#[snafu(implicit)]
location: Location,
},
#[snafu(display("Failed to do procedure task"))]
ProcedureService {
source: BoxedError,
@@ -194,12 +187,6 @@ pub enum Error {
location: Location,
},
#[snafu(display("Missing MetadataSnapshotHandler, not expected"))]
MissingMetadataSnapshotHandler {
#[snafu(implicit)]
location: Location,
},
#[snafu(display("Invalid function args: {}", err_msg))]
InvalidFuncArgs {
err_msg: String,
@@ -264,7 +251,6 @@ impl ErrorExt for Error {
Error::MissingTableMutationHandler { .. }
| Error::MissingProcedureServiceHandler { .. }
| Error::MissingFlowServiceHandler { .. }
| Error::MissingMetadataSnapshotHandler { .. }
| Error::RegisterUdf { .. } => StatusCode::Unexpected,
Error::UnsupportedInputDataType { .. }
@@ -276,8 +262,7 @@ impl ErrorExt for Error {
Error::DecodePlan { source, .. }
| Error::Execute { source, .. }
| Error::ProcedureService { source, .. }
| Error::TableMutation { source, .. }
| Error::MetadataSnapshot { source, .. } => source.status_code(),
| Error::TableMutation { source, .. } => source.status_code(),
Error::PermissionDenied { .. } => StatusCode::PermissionDenied,
}

View File

@@ -18,16 +18,19 @@ mod udaf;
use std::sync::Arc;
use api::v1::TableName;
use datafusion::catalog::CatalogProviderList;
use datafusion::error::Result as DatafusionResult;
use datafusion::logical_expr::{LogicalPlan, LogicalPlanBuilder};
use datafusion_common::Column;
use datafusion_expr::col;
use datafusion_common::{Column, TableReference};
use datafusion_expr::dml::InsertOp;
use datafusion_expr::{col, DmlStatement, WriteOp};
pub use expr::{build_filter_from_timestamp, build_same_type_ts_filter};
use snafu::ResultExt;
pub use self::accumulator::{Accumulator, AggregateFunctionCreator, AggregateFunctionCreatorRef};
pub use self::udaf::AggregateFunction;
use crate::error::Result;
use crate::error::{GeneralDataFusionSnafu, Result};
use crate::logical_plan::accumulator::*;
use crate::signature::{Signature, Volatility};
@@ -79,6 +82,74 @@ pub fn rename_logical_plan_columns(
LogicalPlanBuilder::from(plan).project(projection)?.build()
}
/// Convert a insert into logical plan to an (table_name, logical_plan)
/// where table_name is the name of the table to insert into.
/// logical_plan is the plan to be executed.
///
/// if input logical plan is not `insert into table_name <input>`, return None
///
/// Returned TableName will use provided catalog and schema if not specified in the logical plan,
/// if table scan in logical plan have full table name, will **NOT** override it.
pub fn breakup_insert_plan(
plan: &LogicalPlan,
default_catalog: &str,
default_schema: &str,
) -> Option<(TableName, Arc<LogicalPlan>)> {
if let LogicalPlan::Dml(dml) = plan {
if dml.op != WriteOp::Insert(InsertOp::Append) {
return None;
}
let table_name = &dml.table_name;
let table_name = match table_name {
TableReference::Bare { table } => TableName {
catalog_name: default_catalog.to_string(),
schema_name: default_schema.to_string(),
table_name: table.to_string(),
},
TableReference::Partial { schema, table } => TableName {
catalog_name: default_catalog.to_string(),
schema_name: schema.to_string(),
table_name: table.to_string(),
},
TableReference::Full {
catalog,
schema,
table,
} => TableName {
catalog_name: catalog.to_string(),
schema_name: schema.to_string(),
table_name: table.to_string(),
},
};
let logical_plan = dml.input.clone();
Some((table_name, logical_plan))
} else {
None
}
}
/// create a `insert into table_name <input>` logical plan
pub fn add_insert_to_logical_plan(
table_name: TableName,
table_schema: datafusion_common::DFSchemaRef,
input: LogicalPlan,
) -> Result<LogicalPlan> {
let table_name = TableReference::Full {
catalog: table_name.catalog_name.into(),
schema: table_name.schema_name.into(),
table: table_name.table_name.into(),
};
let plan = LogicalPlan::Dml(DmlStatement::new(
table_name,
table_schema,
WriteOp::Insert(InsertOp::Append),
Arc::new(input),
));
let plan = plan.recompute_schema().context(GeneralDataFusionSnafu)?;
Ok(plan)
}
/// The datafusion `[LogicalPlan]` decoder.
#[async_trait::async_trait]
pub trait SubstraitPlanDecoder {

View File

@@ -23,17 +23,22 @@ use serde::{Deserialize, Serialize};
use snafu::{OptionExt, ResultExt};
/// The default backoff config for kafka client.
///
/// If the operation fails, the client will retry 3 times.
/// The backoff time is 100ms, 300ms, 900ms.
pub const DEFAULT_BACKOFF_CONFIG: BackoffConfig = BackoffConfig {
init_backoff: Duration::from_millis(100),
max_backoff: Duration::from_secs(10),
base: 2.0,
deadline: Some(Duration::from_secs(120)),
max_backoff: Duration::from_secs(1),
base: 3.0,
// The deadline shouldn't be too long,
// otherwise the client will block the worker loop for a long time.
deadline: Some(Duration::from_secs(3)),
};
/// Default interval for active WAL pruning.
pub const DEFAULT_ACTIVE_PRUNE_INTERVAL: Duration = Duration::ZERO;
/// Default limit for concurrent active pruning tasks.
pub const DEFAULT_ACTIVE_PRUNE_TASK_LIMIT: usize = 10;
/// Default interval for auto WAL pruning.
pub const DEFAULT_AUTO_PRUNE_INTERVAL: Duration = Duration::ZERO;
/// Default limit for concurrent auto pruning tasks.
pub const DEFAULT_AUTO_PRUNE_PARALLELISM: usize = 10;
/// Default interval for sending flush request to regions when pruning remote WAL.
pub const DEFAULT_TRIGGER_FLUSH_THRESHOLD: u64 = 0;

View File

@@ -18,8 +18,8 @@ use common_base::readable_size::ReadableSize;
use serde::{Deserialize, Serialize};
use crate::config::kafka::common::{
KafkaConnectionConfig, KafkaTopicConfig, DEFAULT_ACTIVE_PRUNE_INTERVAL,
DEFAULT_ACTIVE_PRUNE_TASK_LIMIT, DEFAULT_TRIGGER_FLUSH_THRESHOLD,
KafkaConnectionConfig, KafkaTopicConfig, DEFAULT_AUTO_PRUNE_INTERVAL,
DEFAULT_AUTO_PRUNE_PARALLELISM, DEFAULT_TRIGGER_FLUSH_THRESHOLD,
};
/// Kafka wal configurations for datanode.
@@ -47,9 +47,8 @@ pub struct DatanodeKafkaConfig {
pub dump_index_interval: Duration,
/// Ignore missing entries during read WAL.
pub overwrite_entry_start_id: bool,
// Active WAL pruning.
pub auto_prune_topic_records: bool,
// Interval of WAL pruning.
#[serde(with = "humantime_serde")]
pub auto_prune_interval: Duration,
// Threshold for sending flush request when pruning remote WAL.
// `None` stands for never sending flush request.
@@ -70,10 +69,9 @@ impl Default for DatanodeKafkaConfig {
create_index: true,
dump_index_interval: Duration::from_secs(60),
overwrite_entry_start_id: false,
auto_prune_topic_records: false,
auto_prune_interval: DEFAULT_ACTIVE_PRUNE_INTERVAL,
auto_prune_interval: DEFAULT_AUTO_PRUNE_INTERVAL,
trigger_flush_threshold: DEFAULT_TRIGGER_FLUSH_THRESHOLD,
auto_prune_parallelism: DEFAULT_ACTIVE_PRUNE_TASK_LIMIT,
auto_prune_parallelism: DEFAULT_AUTO_PRUNE_PARALLELISM,
}
}
}

View File

@@ -17,8 +17,8 @@ use std::time::Duration;
use serde::{Deserialize, Serialize};
use crate::config::kafka::common::{
KafkaConnectionConfig, KafkaTopicConfig, DEFAULT_ACTIVE_PRUNE_INTERVAL,
DEFAULT_ACTIVE_PRUNE_TASK_LIMIT, DEFAULT_TRIGGER_FLUSH_THRESHOLD,
KafkaConnectionConfig, KafkaTopicConfig, DEFAULT_AUTO_PRUNE_INTERVAL,
DEFAULT_AUTO_PRUNE_PARALLELISM, DEFAULT_TRIGGER_FLUSH_THRESHOLD,
};
/// Kafka wal configurations for metasrv.
@@ -34,6 +34,7 @@ pub struct MetasrvKafkaConfig {
// Automatically create topics for WAL.
pub auto_create_topics: bool,
// Interval of WAL pruning.
#[serde(with = "humantime_serde")]
pub auto_prune_interval: Duration,
// Threshold for sending flush request when pruning remote WAL.
// `None` stands for never sending flush request.
@@ -48,9 +49,9 @@ impl Default for MetasrvKafkaConfig {
connection: Default::default(),
kafka_topic: Default::default(),
auto_create_topics: true,
auto_prune_interval: DEFAULT_ACTIVE_PRUNE_INTERVAL,
auto_prune_interval: DEFAULT_AUTO_PRUNE_INTERVAL,
trigger_flush_threshold: DEFAULT_TRIGGER_FLUSH_THRESHOLD,
auto_prune_parallelism: DEFAULT_ACTIVE_PRUNE_TASK_LIMIT,
auto_prune_parallelism: DEFAULT_AUTO_PRUNE_PARALLELISM,
}
}
}

View File

@@ -31,3 +31,33 @@ where
test(endpoints).await
}
/// Get the kafka endpoints from the environment variable `GT_KAFKA_ENDPOINTS`.
///
/// The format of the environment variable is:
/// ```
/// GT_KAFKA_ENDPOINTS=localhost:9092,localhost:9093
/// ```
pub fn get_kafka_endpoints() -> Vec<String> {
let endpoints = std::env::var("GT_KAFKA_ENDPOINTS").unwrap();
endpoints
.split(',')
.map(|s| s.trim().to_string())
.collect::<Vec<_>>()
}
#[macro_export]
/// Skip the test if the environment variable `GT_KAFKA_ENDPOINTS` is not set.
///
/// The format of the environment variable is:
/// ```
/// GT_KAFKA_ENDPOINTS=localhost:9092,localhost:9093
/// ```
macro_rules! maybe_skip_kafka_integration_test {
() => {
if std::env::var("GT_KAFKA_ENDPOINTS").is_err() {
common_telemetry::warn!("The endpoints is empty, skipping the test");
return;
}
};
}

View File

@@ -57,9 +57,9 @@ use tokio::sync::Notify;
use crate::config::{DatanodeOptions, RegionEngineConfig, StorageConfig};
use crate::error::{
self, BuildMitoEngineSnafu, CreateDirSnafu, GetMetadataSnafu, MissingCacheSnafu,
MissingKvBackendSnafu, MissingNodeIdSnafu, OpenLogStoreSnafu, Result, ShutdownInstanceSnafu,
ShutdownServerSnafu, StartServerSnafu,
self, BuildMetricEngineSnafu, BuildMitoEngineSnafu, CreateDirSnafu, GetMetadataSnafu,
MissingCacheSnafu, MissingKvBackendSnafu, MissingNodeIdSnafu, OpenLogStoreSnafu, Result,
ShutdownInstanceSnafu, ShutdownServerSnafu, StartServerSnafu,
};
use crate::event_listener::{
new_region_server_event_channel, NoopRegionServerEventListener, RegionServerEventListenerRef,
@@ -357,7 +357,6 @@ impl DatanodeBuilder {
None,
None,
None,
None,
false,
self.plugins.clone(),
opts.query.clone(),
@@ -399,44 +398,46 @@ impl DatanodeBuilder {
schema_metadata_manager: SchemaMetadataManagerRef,
plugins: Plugins,
) -> Result<Vec<RegionEngineRef>> {
let mut engines = vec![];
let mut metric_engine_config = opts.region_engine.iter().find_map(|c| match c {
RegionEngineConfig::Metric(config) => Some(config.clone()),
_ => None,
});
let mut metric_engine_config = metric_engine::config::EngineConfig::default();
let mut mito_engine_config = MitoConfig::default();
let mut file_engine_config = file_engine::config::EngineConfig::default();
for engine in &opts.region_engine {
match engine {
RegionEngineConfig::Mito(config) => {
let mito_engine = Self::build_mito_engine(
opts,
object_store_manager.clone(),
config.clone(),
schema_metadata_manager.clone(),
plugins.clone(),
)
.await?;
let metric_engine = MetricEngine::new(
mito_engine.clone(),
metric_engine_config.take().unwrap_or_default(),
);
engines.push(Arc::new(mito_engine) as _);
engines.push(Arc::new(metric_engine) as _);
mito_engine_config = config.clone();
}
RegionEngineConfig::File(config) => {
let engine = FileRegionEngine::new(
config.clone(),
object_store_manager.default_object_store().clone(), // TODO: implement custom storage for file engine
);
engines.push(Arc::new(engine) as _);
file_engine_config = config.clone();
}
RegionEngineConfig::Metric(_) => {
// Already handled in `build_mito_engine`.
RegionEngineConfig::Metric(metric_config) => {
metric_engine_config = metric_config.clone();
}
}
}
Ok(engines)
let mito_engine = Self::build_mito_engine(
opts,
object_store_manager.clone(),
mito_engine_config,
schema_metadata_manager.clone(),
plugins.clone(),
)
.await?;
let metric_engine = MetricEngine::try_new(mito_engine.clone(), metric_engine_config)
.context(BuildMetricEngineSnafu)?;
let file_engine = FileRegionEngine::new(
file_engine_config,
object_store_manager.default_object_store().clone(), // TODO: implement custom storage for file engine
);
Ok(vec![
Arc::new(mito_engine) as _,
Arc::new(metric_engine) as _,
Arc::new(file_engine) as _,
])
}
/// Builds [MitoEngine] according to options.

View File

@@ -336,6 +336,13 @@ pub enum Error {
location: Location,
},
#[snafu(display("Failed to build metric engine"))]
BuildMetricEngine {
source: metric_engine::error::Error,
#[snafu(implicit)]
location: Location,
},
#[snafu(display("Failed to serialize options to TOML"))]
TomlFormat {
#[snafu(implicit)]
@@ -452,6 +459,7 @@ impl ErrorExt for Error {
FindLogicalRegions { source, .. } => source.status_code(),
BuildMitoEngine { source, .. } => source.status_code(),
BuildMetricEngine { source, .. } => source.status_code(),
ConcurrentQueryLimiterClosed { .. } | ConcurrentQueryLimiterTimeout { .. } => {
StatusCode::RegionBusy
}

View File

@@ -25,6 +25,7 @@ use std::sync::Arc;
use std::time::Duration;
use common_telemetry::{info, warn};
use mito2::access_layer::{ATOMIC_WRITE_DIR, OLD_ATOMIC_WRITE_DIR};
use object_store::layers::{LruCacheLayer, RetryInterceptor, RetryLayer};
use object_store::services::Fs;
use object_store::util::{join_dir, normalize_dir, with_instrument_layers};
@@ -168,9 +169,13 @@ async fn build_cache_layer(
if let Some(path) = cache_path.as_ref()
&& !path.trim().is_empty()
{
let atomic_temp_dir = join_dir(path, ".tmp/");
let atomic_temp_dir = join_dir(path, ATOMIC_WRITE_DIR);
clean_temp_dir(&atomic_temp_dir)?;
// Compatible code. Remove this after a major release.
let old_atomic_temp_dir = join_dir(path, OLD_ATOMIC_WRITE_DIR);
clean_temp_dir(&old_atomic_temp_dir)?;
let cache_store = Fs::default()
.root(path)
.atomic_write_dir(&atomic_temp_dir)

View File

@@ -15,6 +15,7 @@
use std::{fs, path};
use common_telemetry::info;
use mito2::access_layer::{ATOMIC_WRITE_DIR, OLD_ATOMIC_WRITE_DIR};
use object_store::services::Fs;
use object_store::util::join_dir;
use object_store::ObjectStore;
@@ -33,9 +34,13 @@ pub async fn new_fs_object_store(
.context(error::CreateDirSnafu { dir: data_home })?;
info!("The file storage home is: {}", data_home);
let atomic_write_dir = join_dir(data_home, ".tmp/");
let atomic_write_dir = join_dir(data_home, ATOMIC_WRITE_DIR);
store::clean_temp_dir(&atomic_write_dir)?;
// Compatible code. Remove this after a major release.
let old_atomic_temp_dir = join_dir(data_home, OLD_ATOMIC_WRITE_DIR);
store::clean_temp_dir(&old_atomic_temp_dir)?;
let builder = Fs::default()
.root(data_home)
.atomic_write_dir(&atomic_write_dir);

View File

@@ -16,6 +16,7 @@ async-trait.workspace = true
bytes.workspace = true
cache.workspace = true
catalog.workspace = true
chrono.workspace = true
client.workspace = true
common-base.workspace = true
common-config.workspace = true
@@ -39,16 +40,13 @@ datafusion-expr.workspace = true
datafusion-physical-expr.workspace = true
datafusion-substrait.workspace = true
datatypes.workspace = true
dfir_rs = { version = "0.13.0", default-features = false }
enum-as-inner = "0.6.0"
enum_dispatch = "0.3"
futures.workspace = true
get-size2 = "0.1.2"
greptime-proto.workspace = true
# This fork of hydroflow is simply for keeping our dependency in our org, and pin the version
# otherwise it is the same with upstream repo
chrono.workspace = true
http.workspace = true
hydroflow = { git = "https://github.com/GreptimeTeam/hydroflow.git", branch = "main" }
itertools.workspace = true
lazy_static.workspace = true
meta-client.workspace = true
@@ -60,6 +58,7 @@ partition.workspace = true
prometheus.workspace = true
prost.workspace = true
query.workspace = true
rand.workspace = true
serde.workspace = true
servers.workspace = true
session.workspace = true

View File

@@ -58,7 +58,7 @@ use crate::metrics::{METRIC_FLOW_INSERT_ELAPSED, METRIC_FLOW_ROWS, METRIC_FLOW_R
use crate::repr::{self, DiffRow, RelationDesc, Row, BATCH_SIZE};
use crate::{CreateFlowArgs, FlowId, TableName};
mod flownode_impl;
pub(crate) mod flownode_impl;
mod parse_expr;
pub(crate) mod refill;
mod stat;
@@ -135,12 +135,13 @@ impl Configurable for FlownodeOptions {
}
/// Arc-ed FlowNodeManager, cheaper to clone
pub type FlowWorkerManagerRef = Arc<FlowWorkerManager>;
pub type FlowStreamingEngineRef = Arc<StreamingEngine>;
/// FlowNodeManager manages the state of all tasks in the flow node, which should be run on the same thread
///
/// The choice of timestamp is just using current system timestamp for now
pub struct FlowWorkerManager {
///
pub struct StreamingEngine {
/// The handler to the worker that will run the dataflow
/// which is `!Send` so a handle is used
pub worker_handles: Vec<WorkerHandle>,
@@ -158,7 +159,8 @@ pub struct FlowWorkerManager {
flow_err_collectors: RwLock<BTreeMap<FlowId, ErrCollector>>,
src_send_buf_lens: RwLock<BTreeMap<TableId, watch::Receiver<usize>>>,
tick_manager: FlowTickManager,
node_id: Option<u32>,
/// This node id is only available in distributed mode, on standalone mode this is guaranteed to be `None`
pub node_id: Option<u32>,
/// Lock for flushing, will be `read` by `handle_inserts` and `write` by `flush_flow`
///
/// So that a series of event like `inserts -> flush` can be handled correctly
@@ -168,7 +170,7 @@ pub struct FlowWorkerManager {
}
/// Building FlownodeManager
impl FlowWorkerManager {
impl StreamingEngine {
/// set frontend invoker
pub async fn set_frontend_invoker(&self, frontend: FrontendInvoker) {
*self.frontend_invoker.write().await = Some(frontend);
@@ -187,7 +189,7 @@ impl FlowWorkerManager {
let node_context = FlownodeContext::new(Box::new(srv_map.clone()) as _);
let tick_manager = FlowTickManager::new();
let worker_handles = Vec::new();
FlowWorkerManager {
StreamingEngine {
worker_handles,
worker_selector: Mutex::new(0),
query_engine,
@@ -263,7 +265,7 @@ pub fn batches_to_rows_req(batches: Vec<Batch>) -> Result<Vec<DiffRequest>, Erro
}
/// This impl block contains methods to send writeback requests to frontend
impl FlowWorkerManager {
impl StreamingEngine {
/// Return the number of requests it made
pub async fn send_writeback_requests(&self) -> Result<usize, Error> {
let all_reqs = self.generate_writeback_request().await?;
@@ -534,7 +536,7 @@ impl FlowWorkerManager {
}
/// Flow Runtime related methods
impl FlowWorkerManager {
impl StreamingEngine {
/// Start state report handler, which will receive a sender from HeartbeatTask to send state size report back
///
/// if heartbeat task is shutdown, this future will exit too
@@ -659,7 +661,7 @@ impl FlowWorkerManager {
}
// flow is now shutdown, drop frontend_invoker early so a ref cycle(in standalone mode) can be prevent:
// FlowWorkerManager.frontend_invoker -> FrontendInvoker.inserter
// -> Inserter.node_manager -> NodeManager.flownode -> Flownode.flow_worker_manager.frontend_invoker
// -> Inserter.node_manager -> NodeManager.flownode -> Flownode.flow_streaming_engine.frontend_invoker
self.frontend_invoker.write().await.take();
}
@@ -728,7 +730,7 @@ impl FlowWorkerManager {
}
/// Create&Remove flow
impl FlowWorkerManager {
impl StreamingEngine {
/// remove a flow by it's id
pub async fn remove_flow_inner(&self, flow_id: FlowId) -> Result<(), Error> {
for handle in self.worker_handles.iter() {
@@ -746,7 +748,6 @@ impl FlowWorkerManager {
/// steps to create task:
/// 1. parse query into typed plan(and optional parse expire_after expr)
/// 2. render source/sink with output table id and used input table id
#[allow(clippy::too_many_arguments)]
pub async fn create_flow_inner(&self, args: CreateFlowArgs) -> Result<Option<FlowId>, Error> {
let CreateFlowArgs {
flow_id,

View File

@@ -14,41 +14,511 @@
//! impl `FlowNode` trait for FlowNodeManager so standalone can call them
use std::collections::{HashMap, HashSet};
use std::sync::atomic::AtomicBool;
use std::sync::Arc;
use api::v1::flow::{
flow_request, CreateRequest, DropRequest, FlowRequest, FlowResponse, FlushFlow,
};
use api::v1::region::InsertRequests;
use catalog::CatalogManager;
use common_error::ext::BoxedError;
use common_meta::ddl::create_flow::FlowType;
use common_meta::error::{Result as MetaResult, UnexpectedSnafu};
use common_meta::error::Result as MetaResult;
use common_meta::key::flow::FlowMetadataManager;
use common_runtime::JoinHandle;
use common_telemetry::{trace, warn};
use common_telemetry::{error, info, trace, warn};
use datatypes::value::Value;
use futures::TryStreamExt;
use itertools::Itertools;
use snafu::{IntoError, OptionExt, ResultExt};
use session::context::QueryContextBuilder;
use snafu::{ensure, IntoError, OptionExt, ResultExt};
use store_api::storage::{RegionId, TableId};
use tokio::sync::{Mutex, RwLock};
use crate::adapter::{CreateFlowArgs, FlowWorkerManager};
use crate::adapter::{CreateFlowArgs, StreamingEngine};
use crate::batching_mode::engine::BatchingEngine;
use crate::batching_mode::{FRONTEND_SCAN_TIMEOUT, MIN_REFRESH_DURATION};
use crate::engine::FlowEngine;
use crate::error::{CreateFlowSnafu, FlowNotFoundSnafu, InsertIntoFlowSnafu, InternalSnafu};
use crate::error::{
CreateFlowSnafu, ExternalSnafu, FlowNotFoundSnafu, FlowNotRecoveredSnafu,
IllegalCheckTaskStateSnafu, InsertIntoFlowSnafu, InternalSnafu, JoinTaskSnafu, ListFlowsSnafu,
NoAvailableFrontendSnafu, SyncCheckTaskSnafu, UnexpectedSnafu,
};
use crate::metrics::METRIC_FLOW_TASK_COUNT;
use crate::repr::{self, DiffRow};
use crate::{Error, FlowId};
/// Ref to [`FlowDualEngine`]
pub type FlowDualEngineRef = Arc<FlowDualEngine>;
/// Manage both streaming and batching mode engine
///
/// including create/drop/flush flow
/// and redirect insert requests to the appropriate engine
pub struct FlowDualEngine {
streaming_engine: Arc<FlowWorkerManager>,
streaming_engine: Arc<StreamingEngine>,
batching_engine: Arc<BatchingEngine>,
/// helper struct for faster query flow by table id or vice versa
src_table2flow: std::sync::RwLock<SrcTableToFlow>,
src_table2flow: RwLock<SrcTableToFlow>,
flow_metadata_manager: Arc<FlowMetadataManager>,
catalog_manager: Arc<dyn CatalogManager>,
check_task: tokio::sync::Mutex<Option<ConsistentCheckTask>>,
done_recovering: AtomicBool,
}
impl FlowDualEngine {
pub fn new(
streaming_engine: Arc<StreamingEngine>,
batching_engine: Arc<BatchingEngine>,
flow_metadata_manager: Arc<FlowMetadataManager>,
catalog_manager: Arc<dyn CatalogManager>,
) -> Self {
Self {
streaming_engine,
batching_engine,
src_table2flow: RwLock::new(SrcTableToFlow::default()),
flow_metadata_manager,
catalog_manager,
check_task: Mutex::new(None),
done_recovering: AtomicBool::new(false),
}
}
/// Set `done_recovering` to true
/// indicate that we are ready to handle requests
pub fn set_done_recovering(&self) {
info!("FlowDualEngine done recovering");
self.done_recovering
.store(true, std::sync::atomic::Ordering::Release);
}
/// Check if `done_recovering` is true
pub fn is_recover_done(&self) -> bool {
self.done_recovering
.load(std::sync::atomic::Ordering::Acquire)
}
/// wait for recovering to be done, this will only happen when flownode just started
async fn wait_for_all_flow_recover(&self, waiting_req_cnt: usize) -> Result<(), Error> {
if self.is_recover_done() {
return Ok(());
}
warn!(
"FlowDualEngine is not done recovering, {} insert request waiting for recovery",
waiting_req_cnt
);
// wait 3 seconds, check every 1 second
// TODO(discord9): make this configurable
let mut retry = 0;
let max_retry = 3;
while retry < max_retry && !self.is_recover_done() {
warn!(
"FlowDualEngine is not done recovering, retry {} in 1s",
retry
);
tokio::time::sleep(std::time::Duration::from_secs(1)).await;
retry += 1;
}
if retry == max_retry {
return FlowNotRecoveredSnafu.fail();
} else {
info!("FlowDualEngine is done recovering");
}
// TODO(discord9): also put to centralized logging for flow once it implemented
Ok(())
}
/// Determine if the engine is in distributed mode
pub fn is_distributed(&self) -> bool {
self.streaming_engine.node_id.is_some()
}
pub fn streaming_engine(&self) -> Arc<StreamingEngine> {
self.streaming_engine.clone()
}
pub fn batching_engine(&self) -> Arc<BatchingEngine> {
self.batching_engine.clone()
}
/// In distributed mode, scan periodically(1s) until available frontend is found, or timeout,
/// in standalone mode, return immediately
/// notice here if any frontend appear in cluster info this function will return immediately
async fn wait_for_available_frontend(&self, timeout: std::time::Duration) -> Result<(), Error> {
if !self.is_distributed() {
return Ok(());
}
let frontend_client = self.batching_engine().frontend_client.clone();
let sleep_duration = std::time::Duration::from_millis(1_000);
let now = std::time::Instant::now();
loop {
let frontend_list = frontend_client.scan_for_frontend().await?;
if !frontend_list.is_empty() {
let fe_list = frontend_list
.iter()
.map(|(_, info)| &info.peer.addr)
.collect::<Vec<_>>();
info!("Available frontend found: {:?}", fe_list);
return Ok(());
}
let elapsed = now.elapsed();
tokio::time::sleep(sleep_duration).await;
info!("Waiting for available frontend, elapsed={:?}", elapsed);
if elapsed >= timeout {
return NoAvailableFrontendSnafu {
timeout,
context: "No available frontend found in cluster info",
}
.fail();
}
}
}
/// Try to sync with check task, this is only used in drop flow&flush flow, so a flow id is required
///
/// the need to sync is to make sure flush flow actually get called
async fn try_sync_with_check_task(
&self,
flow_id: FlowId,
allow_drop: bool,
) -> Result<(), Error> {
// this function rarely get called so adding some log is helpful
info!("Try to sync with check task for flow {}", flow_id);
let mut retry = 0;
let max_retry = 10;
// keep trying to trigger consistent check
while retry < max_retry {
if let Some(task) = self.check_task.lock().await.as_ref() {
task.trigger(false, allow_drop).await?;
break;
}
retry += 1;
tokio::time::sleep(std::time::Duration::from_millis(500)).await;
}
if retry == max_retry {
error!(
"Can't sync with check task for flow {} with allow_drop={}",
flow_id, allow_drop
);
return SyncCheckTaskSnafu {
flow_id,
allow_drop,
}
.fail();
}
info!("Successfully sync with check task for flow {}", flow_id);
Ok(())
}
/// Spawn a task to consistently check if all flow tasks in metasrv is created on flownode,
/// so on startup, this will create all missing flow tasks, and constantly check at a interval
async fn check_flow_consistent(
&self,
allow_create: bool,
allow_drop: bool,
) -> Result<(), Error> {
// use nodeid to determine if this is standalone/distributed mode, and retrieve all flows in this node(in distributed mode)/or all flows(in standalone mode)
let nodeid = self.streaming_engine.node_id;
let should_exists: Vec<_> = if let Some(nodeid) = nodeid {
// nodeid is available, so we only need to check flows on this node
// which also means we are in distributed mode
let to_be_recover = self
.flow_metadata_manager
.flownode_flow_manager()
.flows(nodeid.into())
.try_collect::<Vec<_>>()
.await
.context(ListFlowsSnafu {
id: Some(nodeid.into()),
})?;
to_be_recover.into_iter().map(|(id, _)| id).collect()
} else {
// nodeid is not available, so we need to check all flows
// which also means we are in standalone mode
let all_catalogs = self
.catalog_manager
.catalog_names()
.await
.map_err(BoxedError::new)
.context(ExternalSnafu)?;
let mut all_flow_ids = vec![];
for catalog in all_catalogs {
let flows = self
.flow_metadata_manager
.flow_name_manager()
.flow_names(&catalog)
.await
.try_collect::<Vec<_>>()
.await
.map_err(BoxedError::new)
.context(ExternalSnafu)?;
all_flow_ids.extend(flows.into_iter().map(|(_, id)| id.flow_id()));
}
all_flow_ids
};
let should_exists = should_exists
.into_iter()
.map(|i| i as FlowId)
.collect::<HashSet<_>>();
let actual_exists = self.list_flows().await?.into_iter().collect::<HashSet<_>>();
let to_be_created = should_exists
.iter()
.filter(|id| !actual_exists.contains(id))
.collect::<Vec<_>>();
let to_be_dropped = actual_exists
.iter()
.filter(|id| !should_exists.contains(id))
.collect::<Vec<_>>();
if !to_be_created.is_empty() {
if allow_create {
info!(
"Recovering {} flows: {:?}",
to_be_created.len(),
to_be_created
);
let mut errors = vec![];
for flow_id in to_be_created.clone() {
let flow_id = *flow_id;
let info = self
.flow_metadata_manager
.flow_info_manager()
.get(flow_id as u32)
.await
.map_err(BoxedError::new)
.context(ExternalSnafu)?
.context(FlowNotFoundSnafu { id: flow_id })?;
let sink_table_name = [
info.sink_table_name().catalog_name.clone(),
info.sink_table_name().schema_name.clone(),
info.sink_table_name().table_name.clone(),
];
let args = CreateFlowArgs {
flow_id,
sink_table_name,
source_table_ids: info.source_table_ids().to_vec(),
// because recover should only happen on restart the `create_if_not_exists` and `or_replace` can be arbitrary value(since flow doesn't exist)
// but for the sake of consistency and to make sure recover of flow actually happen, we set both to true
// (which is also fine since checks for not allow both to be true is on metasrv and we already pass that)
create_if_not_exists: true,
or_replace: true,
expire_after: info.expire_after(),
comment: Some(info.comment().clone()),
sql: info.raw_sql().clone(),
flow_options: info.options().clone(),
query_ctx: info
.query_context()
.clone()
.map(|ctx| {
ctx.try_into()
.map_err(BoxedError::new)
.context(ExternalSnafu)
})
.transpose()?
// or use default QueryContext with catalog_name from info
// to keep compatibility with old version
.or_else(|| {
Some(
QueryContextBuilder::default()
.current_catalog(info.catalog_name().to_string())
.build(),
)
}),
};
if let Err(err) = self
.create_flow(args)
.await
.map_err(BoxedError::new)
.with_context(|_| CreateFlowSnafu {
sql: info.raw_sql().clone(),
})
{
errors.push((flow_id, err));
}
}
if errors.is_empty() {
info!("Recover flows successfully, flows: {:?}", to_be_created);
}
for (flow_id, err) in errors {
warn!("Failed to recreate flow {}, err={:#?}", flow_id, err);
}
} else {
warn!(
"Flows do not exist in flownode for node {:?}, flow_ids={:?}",
nodeid, to_be_created
);
}
}
if !to_be_dropped.is_empty() {
if allow_drop {
info!("Dropping flows: {:?}", to_be_dropped);
let mut errors = vec![];
for flow_id in to_be_dropped {
let flow_id = *flow_id;
if let Err(err) = self.remove_flow(flow_id).await {
errors.push((flow_id, err));
}
}
for (flow_id, err) in errors {
warn!("Failed to drop flow {}, err={:#?}", flow_id, err);
}
} else {
warn!(
"Flows do not exist in metadata for node {:?}, flow_ids={:?}",
nodeid, to_be_dropped
);
}
}
Ok(())
}
// TODO(discord9): consider sync this with heartbeat(might become necessary in the future)
pub async fn start_flow_consistent_check_task(self: &Arc<Self>) -> Result<(), Error> {
let mut check_task = self.check_task.lock().await;
ensure!(
check_task.is_none(),
IllegalCheckTaskStateSnafu {
reason: "Flow consistent check task already exists",
}
);
let task = ConsistentCheckTask::start_check_task(self).await?;
*check_task = Some(task);
Ok(())
}
pub async fn stop_flow_consistent_check_task(&self) -> Result<(), Error> {
info!("Stopping flow consistent check task");
let mut check_task = self.check_task.lock().await;
ensure!(
check_task.is_some(),
IllegalCheckTaskStateSnafu {
reason: "Flow consistent check task does not exist",
}
);
check_task.take().unwrap().stop().await?;
info!("Stopped flow consistent check task");
Ok(())
}
/// TODO(discord9): also add a `exists` api using flow metadata manager's `exists` method
async fn flow_exist_in_metadata(&self, flow_id: FlowId) -> Result<bool, Error> {
self.flow_metadata_manager
.flow_info_manager()
.get(flow_id as u32)
.await
.map_err(BoxedError::new)
.context(ExternalSnafu)
.map(|info| info.is_some())
}
}
struct ConsistentCheckTask {
handle: JoinHandle<()>,
shutdown_tx: tokio::sync::mpsc::Sender<()>,
trigger_tx: tokio::sync::mpsc::Sender<(bool, bool, tokio::sync::oneshot::Sender<()>)>,
}
impl ConsistentCheckTask {
async fn start_check_task(engine: &Arc<FlowDualEngine>) -> Result<Self, Error> {
let engine = engine.clone();
let (tx, mut rx) = tokio::sync::mpsc::channel(1);
let (trigger_tx, mut trigger_rx) =
tokio::sync::mpsc::channel::<(bool, bool, tokio::sync::oneshot::Sender<()>)>(10);
let handle = common_runtime::spawn_global(async move {
// first check if available frontend is found
if let Err(err) = engine
.wait_for_available_frontend(FRONTEND_SCAN_TIMEOUT)
.await
{
warn!("No frontend is available yet:\n {err:?}");
}
// then do recover flows, if failed, always retry
let mut recover_retry = 0;
while let Err(err) = engine.check_flow_consistent(true, false).await {
recover_retry += 1;
error!(
"Failed to recover flows:\n {err:?}, retry {} in {}s",
recover_retry,
MIN_REFRESH_DURATION.as_secs()
);
tokio::time::sleep(MIN_REFRESH_DURATION).await;
}
engine.set_done_recovering();
// then do check flows, with configurable allow_create and allow_drop
let (mut allow_create, mut allow_drop) = (false, false);
let mut ret_signal: Option<tokio::sync::oneshot::Sender<()>> = None;
loop {
if let Err(err) = engine.check_flow_consistent(allow_create, allow_drop).await {
error!(err; "Failed to check flow consistent");
}
if let Some(done) = ret_signal.take() {
let _ = done.send(());
}
tokio::select! {
_ = rx.recv() => break,
incoming = trigger_rx.recv() => if let Some(incoming) = incoming {
(allow_create, allow_drop) = (incoming.0, incoming.1);
ret_signal = Some(incoming.2);
},
_ = tokio::time::sleep(std::time::Duration::from_secs(10)) => {
(allow_create, allow_drop) = (false, false);
},
}
}
});
Ok(ConsistentCheckTask {
handle,
shutdown_tx: tx,
trigger_tx,
})
}
async fn trigger(&self, allow_create: bool, allow_drop: bool) -> Result<(), Error> {
let (tx, rx) = tokio::sync::oneshot::channel();
self.trigger_tx
.send((allow_create, allow_drop, tx))
.await
.map_err(|_| {
IllegalCheckTaskStateSnafu {
reason: "Failed to send trigger signal",
}
.build()
})?;
rx.await.map_err(|_| {
IllegalCheckTaskStateSnafu {
reason: "Failed to receive trigger signal",
}
.build()
})?;
Ok(())
}
async fn stop(self) -> Result<(), Error> {
self.shutdown_tx.send(()).await.map_err(|_| {
IllegalCheckTaskStateSnafu {
reason: "Failed to send shutdown signal",
}
.build()
})?;
// abort so no need to wait
self.handle.abort();
Ok(())
}
}
#[derive(Default)]
struct SrcTableToFlow {
/// mapping of table ids to flow ids for streaming mode
stream: HashMap<TableId, HashSet<FlowId>>,
@@ -138,35 +608,54 @@ impl FlowEngine for FlowDualEngine {
self.src_table2flow
.write()
.unwrap()
.await
.add_flow(flow_id, flow_type, src_table_ids);
Ok(res)
}
async fn remove_flow(&self, flow_id: FlowId) -> Result<(), Error> {
let flow_type = self.src_table2flow.read().unwrap().get_flow_type(flow_id);
let flow_type = self.src_table2flow.read().await.get_flow_type(flow_id);
match flow_type {
Some(FlowType::Batching) => self.batching_engine.remove_flow(flow_id).await,
Some(FlowType::Streaming) => self.streaming_engine.remove_flow(flow_id).await,
None => FlowNotFoundSnafu { id: flow_id }.fail(),
None => {
// this can happen if flownode just restart, and is stilling creating the flow
// since now that this flow should dropped, we need to trigger the consistent check and allow drop
// this rely on drop flow ddl delete metadata first, see src/common/meta/src/ddl/drop_flow.rs
warn!(
"Flow {} is not exist in the underlying engine, but exist in metadata",
flow_id
);
self.try_sync_with_check_task(flow_id, true).await?;
Ok(())
}
}?;
// remove mapping
self.src_table2flow.write().unwrap().remove_flow(flow_id);
self.src_table2flow.write().await.remove_flow(flow_id);
Ok(())
}
async fn flush_flow(&self, flow_id: FlowId) -> Result<usize, Error> {
let flow_type = self.src_table2flow.read().unwrap().get_flow_type(flow_id);
// sync with check task
self.try_sync_with_check_task(flow_id, false).await?;
let flow_type = self.src_table2flow.read().await.get_flow_type(flow_id);
match flow_type {
Some(FlowType::Batching) => self.batching_engine.flush_flow(flow_id).await,
Some(FlowType::Streaming) => self.streaming_engine.flush_flow(flow_id).await,
None => FlowNotFoundSnafu { id: flow_id }.fail(),
None => {
warn!(
"Currently flow={flow_id} doesn't exist in flownode, ignore flush_flow request"
);
Ok(0)
}
}
}
async fn flow_exist(&self, flow_id: FlowId) -> Result<bool, Error> {
let flow_type = self.src_table2flow.read().unwrap().get_flow_type(flow_id);
let flow_type = self.src_table2flow.read().await.get_flow_type(flow_id);
// not using `flow_type.is_some()` to make sure the flow is actually exist in the underlying engine
match flow_type {
Some(FlowType::Batching) => self.batching_engine.flow_exist(flow_id).await,
@@ -175,16 +664,26 @@ impl FlowEngine for FlowDualEngine {
}
}
async fn list_flows(&self) -> Result<impl IntoIterator<Item = FlowId>, Error> {
let stream_flows = self.streaming_engine.list_flows().await?;
let batch_flows = self.batching_engine.list_flows().await?;
Ok(stream_flows.into_iter().chain(batch_flows))
}
async fn handle_flow_inserts(
&self,
request: api::v1::region::InsertRequests,
) -> Result<(), Error> {
self.wait_for_all_flow_recover(request.requests.len())
.await?;
// TODO(discord9): make as little clone as possible
let mut to_stream_engine = Vec::with_capacity(request.requests.len());
let mut to_batch_engine = request.requests;
{
let src_table2flow = self.src_table2flow.read().unwrap();
// not locking this, or recover flows will be starved when also handling flow inserts
let src_table2flow = self.src_table2flow.read().await;
to_batch_engine.retain(|req| {
let region_id = RegionId::from(req.region_id);
let table_id = region_id.table_id();
@@ -221,12 +720,7 @@ impl FlowEngine for FlowDualEngine {
requests: to_batch_engine,
})
.await?;
stream_handler.await.map_err(|e| {
crate::error::UnexpectedSnafu {
reason: format!("JoinError when handle inserts for flow stream engine: {e:?}"),
}
.build()
})??;
stream_handler.await.context(JoinTaskSnafu)??;
Ok(())
}
@@ -307,14 +801,7 @@ impl common_meta::node_manager::Flownode for FlowDualEngine {
..Default::default()
})
}
None => UnexpectedSnafu {
err_msg: "Missing request body",
}
.fail(),
_ => UnexpectedSnafu {
err_msg: "Invalid request body.",
}
.fail(),
other => common_meta::error::InvalidFlowRequestBodySnafu { body: other }.fail(),
}
}
@@ -331,15 +818,23 @@ fn to_meta_err(
location: snafu::Location,
) -> impl FnOnce(crate::error::Error) -> common_meta::error::Error {
move |err: crate::error::Error| -> common_meta::error::Error {
common_meta::error::Error::External {
location,
source: BoxedError::new(err),
match err {
crate::error::Error::FlowNotFound { id, .. } => {
common_meta::error::Error::FlowNotFound {
flow_name: format!("flow_id={id}"),
location,
}
}
_ => common_meta::error::Error::External {
location,
source: BoxedError::new(err),
},
}
}
}
#[async_trait::async_trait]
impl common_meta::node_manager::Flownode for FlowWorkerManager {
impl common_meta::node_manager::Flownode for StreamingEngine {
async fn handle(&self, request: FlowRequest) -> MetaResult<FlowResponse> {
let query_ctx = request
.header
@@ -413,14 +908,7 @@ impl common_meta::node_manager::Flownode for FlowWorkerManager {
..Default::default()
})
}
None => UnexpectedSnafu {
err_msg: "Missing request body",
}
.fail(),
_ => UnexpectedSnafu {
err_msg: "Invalid request body.",
}
.fail(),
other => common_meta::error::InvalidFlowRequestBodySnafu { body: other }.fail(),
}
}
@@ -432,7 +920,7 @@ impl common_meta::node_manager::Flownode for FlowWorkerManager {
}
}
impl FlowEngine for FlowWorkerManager {
impl FlowEngine for StreamingEngine {
async fn create_flow(&self, args: CreateFlowArgs) -> Result<Option<FlowId>, Error> {
self.create_flow_inner(args).await
}
@@ -449,6 +937,16 @@ impl FlowEngine for FlowWorkerManager {
self.flow_exist_inner(flow_id).await
}
async fn list_flows(&self) -> Result<impl IntoIterator<Item = FlowId>, Error> {
Ok(self
.flow_err_collectors
.read()
.await
.keys()
.cloned()
.collect::<Vec<_>>())
}
async fn handle_flow_inserts(
&self,
request: api::v1::region::InsertRequests,
@@ -474,7 +972,7 @@ impl FetchFromRow {
}
}
impl FlowWorkerManager {
impl StreamingEngine {
async fn handle_inserts_inner(
&self,
request: InsertRequests,
@@ -552,7 +1050,7 @@ impl FlowWorkerManager {
.copied()
.map(FetchFromRow::Idx)
.or_else(|| col_default_val.clone().map(FetchFromRow::Default))
.with_context(|| crate::error::UnexpectedSnafu {
.with_context(|| UnexpectedSnafu {
reason: format!(
"Column not found: {}, default_value: {:?}",
col_name, col_default_val

View File

@@ -31,7 +31,7 @@ use snafu::{ensure, OptionExt, ResultExt};
use table::metadata::TableId;
use crate::adapter::table_source::ManagedTableSource;
use crate::adapter::{FlowId, FlowWorkerManager, FlowWorkerManagerRef};
use crate::adapter::{FlowId, FlowStreamingEngineRef, StreamingEngine};
use crate::error::{FlowNotFoundSnafu, JoinTaskSnafu, UnexpectedSnafu};
use crate::expr::error::ExternalSnafu;
use crate::expr::utils::find_plan_time_window_expr_lower_bound;
@@ -39,10 +39,10 @@ use crate::repr::RelationDesc;
use crate::server::get_all_flow_ids;
use crate::{Error, FrontendInvoker};
impl FlowWorkerManager {
impl StreamingEngine {
/// Create and start refill flow tasks in background
pub async fn create_and_start_refill_flow_tasks(
self: &FlowWorkerManagerRef,
self: &FlowStreamingEngineRef,
flow_metadata_manager: &FlowMetadataManagerRef,
catalog_manager: &CatalogManagerRef,
) -> Result<(), Error> {
@@ -130,7 +130,7 @@ impl FlowWorkerManager {
/// Starting to refill flows, if any error occurs, will rebuild the flow and retry
pub(crate) async fn starting_refill_flows(
self: &FlowWorkerManagerRef,
self: &FlowStreamingEngineRef,
tasks: Vec<RefillTask>,
) -> Result<(), Error> {
// TODO(discord9): add a back pressure mechanism
@@ -266,7 +266,7 @@ impl TaskState<()> {
fn start_running(
&mut self,
task_data: &TaskData,
manager: FlowWorkerManagerRef,
manager: FlowStreamingEngineRef,
mut output_stream: SendableRecordBatchStream,
) -> Result<(), Error> {
let data = (*task_data).clone();
@@ -383,7 +383,7 @@ impl RefillTask {
/// Start running the task in background, non-blocking
pub async fn start_running(
&mut self,
manager: FlowWorkerManagerRef,
manager: FlowStreamingEngineRef,
invoker: &FrontendInvoker,
) -> Result<(), Error> {
let TaskState::Prepared { sql } = &mut self.state else {

View File

@@ -16,9 +16,9 @@ use std::collections::BTreeMap;
use common_meta::key::flow::flow_state::FlowStat;
use crate::FlowWorkerManager;
use crate::StreamingEngine;
impl FlowWorkerManager {
impl StreamingEngine {
pub async fn gen_state_report(&self) -> FlowStat {
let mut full_report = BTreeMap::new();
let mut last_exec_time_map = BTreeMap::new();

View File

@@ -33,8 +33,8 @@ use crate::adapter::table_source::TableDesc;
use crate::adapter::{TableName, WorkerHandle, AUTO_CREATED_PLACEHOLDER_TS_COL};
use crate::error::{Error, ExternalSnafu, UnexpectedSnafu};
use crate::repr::{ColumnType, RelationDesc, RelationType};
use crate::FlowWorkerManager;
impl FlowWorkerManager {
use crate::StreamingEngine;
impl StreamingEngine {
/// Get a worker handle for creating flow, using round robin to select a worker
pub(crate) async fn get_worker_handle_for_create_flow(&self) -> &WorkerHandle {
let use_idx = {

View File

@@ -19,8 +19,8 @@ use std::sync::atomic::{AtomicBool, Ordering};
use std::sync::Arc;
use common_telemetry::info;
use dfir_rs::scheduled::graph::Dfir;
use enum_as_inner::EnumAsInner;
use hydroflow::scheduled::graph::Hydroflow;
use snafu::ensure;
use tokio::sync::{broadcast, mpsc, oneshot, Mutex};
@@ -49,9 +49,9 @@ pub fn create_worker<'a>() -> (WorkerHandle, Worker<'a>) {
(worker_handle, worker)
}
/// ActiveDataflowState is a wrapper around `Hydroflow` and `DataflowState`
/// ActiveDataflowState is a wrapper around `Dfir` and `DataflowState`
pub(crate) struct ActiveDataflowState<'subgraph> {
df: Hydroflow<'subgraph>,
df: Dfir<'subgraph>,
state: DataflowState,
err_collector: ErrCollector,
}
@@ -59,7 +59,7 @@ pub(crate) struct ActiveDataflowState<'subgraph> {
impl std::fmt::Debug for ActiveDataflowState<'_> {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("ActiveDataflowState")
.field("df", &"<Hydroflow>")
.field("df", &"<Dfir>")
.field("state", &self.state)
.field("err_collector", &self.err_collector)
.finish()
@@ -69,7 +69,7 @@ impl std::fmt::Debug for ActiveDataflowState<'_> {
impl Default for ActiveDataflowState<'_> {
fn default() -> Self {
ActiveDataflowState {
df: Hydroflow::new(),
df: Dfir::new(),
state: DataflowState::default(),
err_collector: ErrCollector::default(),
}

View File

@@ -31,4 +31,19 @@ pub const DEFAULT_BATCHING_ENGINE_QUERY_TIMEOUT: Duration = Duration::from_secs(
pub const SLOW_QUERY_THRESHOLD: Duration = Duration::from_secs(60);
/// The minimum duration between two queries execution by batching mode task
const MIN_REFRESH_DURATION: Duration = Duration::new(5, 0);
pub const MIN_REFRESH_DURATION: Duration = Duration::new(5, 0);
/// Grpc connection timeout
const GRPC_CONN_TIMEOUT: Duration = Duration::from_secs(5);
/// Grpc max retry number
const GRPC_MAX_RETRIES: u32 = 3;
/// Flow wait for available frontend timeout,
/// if failed to find available frontend after FRONTEND_SCAN_TIMEOUT elapsed, return error
/// which should prevent flownode from starting
pub const FRONTEND_SCAN_TIMEOUT: Duration = Duration::from_secs(30);
/// Frontend activity timeout
/// if frontend is down(not sending heartbeat) for more than FRONTEND_ACTIVITY_TIMEOUT, it will be removed from the list that flownode use to connect
pub const FRONTEND_ACTIVITY_TIMEOUT: Duration = Duration::from_secs(60);

View File

@@ -17,14 +17,16 @@
use std::collections::{BTreeMap, HashMap};
use std::sync::Arc;
use catalog::CatalogManagerRef;
use common_error::ext::BoxedError;
use common_meta::ddl::create_flow::FlowType;
use common_meta::key::flow::FlowMetadataManagerRef;
use common_meta::key::table_info::TableInfoManager;
use common_meta::key::table_info::{TableInfoManager, TableInfoValue};
use common_meta::key::TableMetadataManagerRef;
use common_runtime::JoinHandle;
use common_telemetry::info;
use common_telemetry::tracing::warn;
use common_telemetry::{debug, info};
use common_time::TimeToLive;
use query::QueryEngineRef;
use snafu::{ensure, OptionExt, ResultExt};
use store_api::storage::RegionId;
@@ -36,7 +38,10 @@ use crate::batching_mode::task::BatchingTask;
use crate::batching_mode::time_window::{find_time_window_expr, TimeWindowExpr};
use crate::batching_mode::utils::sql_to_df_plan;
use crate::engine::FlowEngine;
use crate::error::{ExternalSnafu, FlowAlreadyExistSnafu, TableNotFoundMetaSnafu, UnexpectedSnafu};
use crate::error::{
ExternalSnafu, FlowAlreadyExistSnafu, FlowNotFoundSnafu, TableNotFoundMetaSnafu,
UnexpectedSnafu, UnsupportedSnafu,
};
use crate::{CreateFlowArgs, Error, FlowId, TableName};
/// Batching mode Engine, responsible for driving all the batching mode tasks
@@ -45,9 +50,11 @@ use crate::{CreateFlowArgs, Error, FlowId, TableName};
pub struct BatchingEngine {
tasks: RwLock<BTreeMap<FlowId, BatchingTask>>,
shutdown_txs: RwLock<BTreeMap<FlowId, oneshot::Sender<()>>>,
frontend_client: Arc<FrontendClient>,
/// frontend client for insert request
pub(crate) frontend_client: Arc<FrontendClient>,
flow_metadata_manager: FlowMetadataManagerRef,
table_meta: TableMetadataManagerRef,
catalog_manager: CatalogManagerRef,
query_engine: QueryEngineRef,
}
@@ -57,6 +64,7 @@ impl BatchingEngine {
query_engine: QueryEngineRef,
flow_metadata_manager: FlowMetadataManagerRef,
table_meta: TableMetadataManagerRef,
catalog_manager: CatalogManagerRef,
) -> Self {
Self {
tasks: Default::default(),
@@ -64,6 +72,7 @@ impl BatchingEngine {
frontend_client,
flow_metadata_manager,
table_meta,
catalog_manager,
query_engine,
}
}
@@ -179,6 +188,16 @@ async fn get_table_name(
table_info: &TableInfoManager,
table_id: &TableId,
) -> Result<TableName, Error> {
get_table_info(table_info, table_id).await.map(|info| {
let name = info.table_name();
[name.catalog_name, name.schema_name, name.table_name]
})
}
async fn get_table_info(
table_info: &TableInfoManager,
table_id: &TableId,
) -> Result<TableInfoValue, Error> {
table_info
.get(*table_id)
.await
@@ -187,8 +206,7 @@ async fn get_table_name(
.with_context(|| UnexpectedSnafu {
reason: format!("Table id = {:?}, couldn't found table name", table_id),
})
.map(|name| name.table_name())
.map(|name| [name.catalog_name, name.schema_name, name.table_name])
.map(|info| info.into_inner())
}
impl BatchingEngine {
@@ -248,7 +266,20 @@ impl BatchingEngine {
let query_ctx = Arc::new(query_ctx);
let mut source_table_names = Vec::with_capacity(2);
for src_id in source_table_ids {
// also check table option to see if ttl!=instant
let table_name = get_table_name(self.table_meta.table_info_manager(), &src_id).await?;
let table_info = get_table_info(self.table_meta.table_info_manager(), &src_id).await?;
ensure!(
table_info.table_info.meta.options.ttl != Some(TimeToLive::Instant),
UnsupportedSnafu {
reason: format!(
"Source table `{}`(id={}) has instant TTL, Instant TTL is not supported under batching mode. Consider using a TTL longer than flush interval",
table_name.join("."),
src_id
),
}
);
source_table_names.push(table_name);
}
@@ -273,7 +304,14 @@ impl BatchingEngine {
})
.transpose()?;
info!("Flow id={}, found time window expr={:?}", flow_id, phy_expr);
debug!(
"Flow id={}, found time window expr={}",
flow_id,
phy_expr
.as_ref()
.map(|phy_expr| phy_expr.to_string())
.unwrap_or("None".to_string())
);
let task = BatchingTask::new(
flow_id,
@@ -284,7 +322,7 @@ impl BatchingEngine {
sink_table_name,
source_table_names,
query_ctx,
self.table_meta.clone(),
self.catalog_manager.clone(),
rx,
);
@@ -293,12 +331,13 @@ impl BatchingEngine {
let frontend = self.frontend_client.clone();
// check execute once first to detect any error early
task.check_execute(&engine, &frontend).await?;
task.check_or_create_sink_table(&engine, &frontend).await?;
// TODO(discord9): also save handle & use time wheel or what for better
let _handle = common_runtime::spawn_global(async move {
// TODO(discord9): use time wheel or what for better
let handle = common_runtime::spawn_global(async move {
task_inner.start_executing_loop(engine, frontend).await;
});
task.state.write().unwrap().task_handle = Some(handle);
// only replace here not earlier because we want the old one intact if something went wrong before this line
let replaced_old_task_opt = self.tasks.write().await.insert(flow_id, task);
@@ -311,7 +350,8 @@ impl BatchingEngine {
pub async fn remove_flow_inner(&self, flow_id: FlowId) -> Result<(), Error> {
if self.tasks.write().await.remove(&flow_id).is_none() {
warn!("Flow {flow_id} not found in tasks")
warn!("Flow {flow_id} not found in tasks");
FlowNotFoundSnafu { id: flow_id }.fail()?;
}
let Some(tx) = self.shutdown_txs.write().await.remove(&flow_id) else {
UnexpectedSnafu {
@@ -326,15 +366,21 @@ impl BatchingEngine {
}
pub async fn flush_flow_inner(&self, flow_id: FlowId) -> Result<usize, Error> {
debug!("Try flush flow {flow_id}");
let task = self.tasks.read().await.get(&flow_id).cloned();
let task = task.with_context(|| UnexpectedSnafu {
reason: format!("Can't found task for flow {flow_id}"),
})?;
let task = task.with_context(|| FlowNotFoundSnafu { id: flow_id })?;
task.mark_all_windows_as_dirty()?;
let res = task
.gen_exec_once(&self.query_engine, &self.frontend_client)
.await?;
let affected_rows = res.map(|(r, _)| r).unwrap_or_default() as usize;
debug!(
"Successfully flush flow {flow_id}, affected rows={}",
affected_rows
);
Ok(affected_rows)
}
@@ -357,6 +403,9 @@ impl FlowEngine for BatchingEngine {
async fn flow_exist(&self, flow_id: FlowId) -> Result<bool, Error> {
Ok(self.flow_exist_inner(flow_id).await)
}
async fn list_flows(&self) -> Result<impl IntoIterator<Item = FlowId>, Error> {
Ok(self.tasks.read().await.keys().cloned().collect::<Vec<_>>())
}
async fn handle_flow_inserts(
&self,
request: api::v1::region::InsertRequests,

View File

@@ -14,44 +14,113 @@
//! Frontend client to run flow as batching task which is time-window-aware normal query triggered every tick set by user
use std::sync::Arc;
use std::sync::{Arc, Weak};
use std::time::SystemTime;
use client::{Client, Database, DEFAULT_CATALOG_NAME, DEFAULT_SCHEMA_NAME};
use common_error::ext::BoxedError;
use api::v1::greptime_request::Request;
use api::v1::CreateTableExpr;
use client::{Client, Database};
use common_error::ext::{BoxedError, ErrorExt};
use common_grpc::channel_manager::{ChannelConfig, ChannelManager};
use common_meta::cluster::{NodeInfo, NodeInfoKey, Role};
use common_meta::peer::Peer;
use common_meta::rpc::store::RangeRequest;
use common_query::Output;
use common_telemetry::warn;
use meta_client::client::MetaClient;
use snafu::ResultExt;
use rand::rng;
use rand::seq::SliceRandom;
use servers::query_handler::grpc::GrpcQueryHandler;
use session::context::{QueryContextBuilder, QueryContextRef};
use snafu::{OptionExt, ResultExt};
use crate::batching_mode::DEFAULT_BATCHING_ENGINE_QUERY_TIMEOUT;
use crate::error::{ExternalSnafu, UnexpectedSnafu};
use crate::batching_mode::{
DEFAULT_BATCHING_ENGINE_QUERY_TIMEOUT, FRONTEND_ACTIVITY_TIMEOUT, GRPC_CONN_TIMEOUT,
GRPC_MAX_RETRIES,
};
use crate::error::{ExternalSnafu, InvalidRequestSnafu, NoAvailableFrontendSnafu, UnexpectedSnafu};
use crate::Error;
fn default_channel_mgr() -> ChannelManager {
let cfg = ChannelConfig::new().timeout(DEFAULT_BATCHING_ENGINE_QUERY_TIMEOUT);
ChannelManager::with_config(cfg)
/// Just like [`GrpcQueryHandler`] but use BoxedError
///
/// basically just a specialized `GrpcQueryHandler<Error=BoxedError>`
///
/// this is only useful for flownode to
/// invoke frontend Instance in standalone mode
#[async_trait::async_trait]
pub trait GrpcQueryHandlerWithBoxedError: Send + Sync + 'static {
async fn do_query(
&self,
query: Request,
ctx: QueryContextRef,
) -> std::result::Result<Output, BoxedError>;
}
fn client_from_urls(addrs: Vec<String>) -> Client {
Client::with_manager_and_urls(default_channel_mgr(), addrs)
/// auto impl
#[async_trait::async_trait]
impl<
E: ErrorExt + Send + Sync + 'static,
T: GrpcQueryHandler<Error = E> + Send + Sync + 'static,
> GrpcQueryHandlerWithBoxedError for T
{
async fn do_query(
&self,
query: Request,
ctx: QueryContextRef,
) -> std::result::Result<Output, BoxedError> {
self.do_query(query, ctx).await.map_err(BoxedError::new)
}
}
type HandlerMutable = Arc<std::sync::Mutex<Option<Weak<dyn GrpcQueryHandlerWithBoxedError>>>>;
/// A simple frontend client able to execute sql using grpc protocol
#[derive(Debug)]
///
/// This is for computation-heavy query which need to offload computation to frontend, lifting the load from flownode
#[derive(Debug, Clone)]
pub enum FrontendClient {
Distributed {
meta_client: Arc<MetaClient>,
chnl_mgr: ChannelManager,
},
Standalone {
/// for the sake of simplicity still use grpc even in standalone mode
/// notice the client here should all be lazy, so that can wait after frontend is booted then make conn
/// TODO(discord9): not use grpc under standalone mode
database_client: DatabaseWithPeer,
database_client: HandlerMutable,
},
}
impl FrontendClient {
/// Create a new empty frontend client, with a `HandlerMutable` to set the grpc handler later
pub fn from_empty_grpc_handler() -> (Self, HandlerMutable) {
let handler = Arc::new(std::sync::Mutex::new(None));
(
Self::Standalone {
database_client: handler.clone(),
},
handler,
)
}
pub fn from_meta_client(meta_client: Arc<MetaClient>) -> Self {
Self::Distributed {
meta_client,
chnl_mgr: {
let cfg = ChannelConfig::new()
.connect_timeout(GRPC_CONN_TIMEOUT)
.timeout(DEFAULT_BATCHING_ENGINE_QUERY_TIMEOUT);
ChannelManager::with_config(cfg)
},
}
}
pub fn from_grpc_handler(grpc_handler: Weak<dyn GrpcQueryHandlerWithBoxedError>) -> Self {
Self::Standalone {
database_client: Arc::new(std::sync::Mutex::new(Some(grpc_handler))),
}
}
}
#[derive(Debug, Clone)]
pub struct DatabaseWithPeer {
pub database: Database,
@@ -62,29 +131,24 @@ impl DatabaseWithPeer {
fn new(database: Database, peer: Peer) -> Self {
Self { database, peer }
}
}
impl FrontendClient {
pub fn from_meta_client(meta_client: Arc<MetaClient>) -> Self {
Self::Distributed { meta_client }
}
pub fn from_static_grpc_addr(addr: String) -> Self {
let peer = Peer {
id: 0,
addr: addr.clone(),
};
let client = client_from_urls(vec![addr]);
let database = Database::new(DEFAULT_CATALOG_NAME, DEFAULT_SCHEMA_NAME, client);
Self::Standalone {
database_client: DatabaseWithPeer::new(database, peer),
}
/// Try sending a "SELECT 1" to the database
async fn try_select_one(&self) -> Result<(), Error> {
// notice here use `sql` for `SELECT 1` return 1 row
let _ = self
.database
.sql("SELECT 1")
.await
.with_context(|_| InvalidRequestSnafu {
context: format!("Failed to handle `SELECT 1` request at {:?}", self.peer),
})?;
Ok(())
}
}
impl FrontendClient {
async fn scan_for_frontend(&self) -> Result<Vec<(NodeInfoKey, NodeInfo)>, Error> {
/// scan for available frontend from metadata
pub(crate) async fn scan_for_frontend(&self) -> Result<Vec<(NodeInfoKey, NodeInfo)>, Error> {
let Self::Distributed { meta_client, .. } = self else {
return Ok(vec![]);
};
@@ -114,35 +178,178 @@ impl FrontendClient {
Ok(res)
}
/// Get the database with max `last_activity_ts`
async fn get_last_active_frontend(&self) -> Result<DatabaseWithPeer, Error> {
if let Self::Standalone { database_client } = self {
return Ok(database_client.clone());
}
let frontends = self.scan_for_frontend().await?;
let mut peer = None;
if let Some((_, val)) = frontends.iter().max_by_key(|(_, val)| val.last_activity_ts) {
peer = Some(val.peer.clone());
}
let Some(peer) = peer else {
UnexpectedSnafu {
reason: format!("No frontend available: {:?}", frontends),
/// Get the frontend with recent enough(less than 1 minute from now) `last_activity_ts`
/// and is able to process query
async fn get_random_active_frontend(
&self,
catalog: &str,
schema: &str,
) -> Result<DatabaseWithPeer, Error> {
let Self::Distributed {
meta_client: _,
chnl_mgr,
} = self
else {
return UnexpectedSnafu {
reason: "Expect distributed mode",
}
.fail()?
.fail();
};
let client = client_from_urls(vec![peer.addr.clone()]);
let database = Database::new(DEFAULT_CATALOG_NAME, DEFAULT_SCHEMA_NAME, client);
Ok(DatabaseWithPeer::new(database, peer))
let mut interval = tokio::time::interval(GRPC_CONN_TIMEOUT);
interval.tick().await;
for retry in 0..GRPC_MAX_RETRIES {
let mut frontends = self.scan_for_frontend().await?;
let now_in_ms = SystemTime::now()
.duration_since(SystemTime::UNIX_EPOCH)
.unwrap()
.as_millis() as i64;
// shuffle the frontends to avoid always pick the same one
frontends.shuffle(&mut rng());
// found node with maximum last_activity_ts
for (_, node_info) in frontends
.iter()
// filter out frontend that have been down for more than 1 min
.filter(|(_, node_info)| {
node_info.last_activity_ts + FRONTEND_ACTIVITY_TIMEOUT.as_millis() as i64
> now_in_ms
})
{
let addr = &node_info.peer.addr;
let client = Client::with_manager_and_urls(chnl_mgr.clone(), vec![addr.clone()]);
let database = Database::new(catalog, schema, client);
let db = DatabaseWithPeer::new(database, node_info.peer.clone());
match db.try_select_one().await {
Ok(_) => return Ok(db),
Err(e) => {
warn!(
"Failed to connect to frontend {} on retry={}: \n{e:?}",
addr, retry
);
}
}
}
// no available frontend
// sleep and retry
interval.tick().await;
}
NoAvailableFrontendSnafu {
timeout: GRPC_CONN_TIMEOUT,
context: "No available frontend found that is able to process query",
}
.fail()
}
/// Get a database client, and possibly update it before returning.
pub async fn get_database_client(&self) -> Result<DatabaseWithPeer, Error> {
pub async fn create(
&self,
create: CreateTableExpr,
catalog: &str,
schema: &str,
) -> Result<u32, Error> {
self.handle(
Request::Ddl(api::v1::DdlRequest {
expr: Some(api::v1::ddl_request::Expr::CreateTable(create)),
}),
catalog,
schema,
&mut None,
)
.await
}
/// Handle a request to frontend
pub(crate) async fn handle(
&self,
req: api::v1::greptime_request::Request,
catalog: &str,
schema: &str,
peer_desc: &mut Option<PeerDesc>,
) -> Result<u32, Error> {
match self {
Self::Standalone { database_client } => Ok(database_client.clone()),
Self::Distributed { meta_client: _ } => self.get_last_active_frontend().await,
FrontendClient::Distributed { .. } => {
let db = self.get_random_active_frontend(catalog, schema).await?;
*peer_desc = Some(PeerDesc::Dist {
peer: db.peer.clone(),
});
db.database
.handle_with_retry(req.clone(), GRPC_MAX_RETRIES)
.await
.with_context(|_| InvalidRequestSnafu {
context: format!("Failed to handle request at {:?}: {:?}", db.peer, req),
})
}
FrontendClient::Standalone { database_client } => {
let ctx = QueryContextBuilder::default()
.current_catalog(catalog.to_string())
.current_schema(schema.to_string())
.build();
let ctx = Arc::new(ctx);
{
let database_client = {
database_client
.lock()
.map_err(|e| {
UnexpectedSnafu {
reason: format!("Failed to lock database client: {e}"),
}
.build()
})?
.as_ref()
.context(UnexpectedSnafu {
reason: "Standalone's frontend instance is not set",
})?
.upgrade()
.context(UnexpectedSnafu {
reason: "Failed to upgrade database client",
})?
};
let resp: common_query::Output = database_client
.do_query(req.clone(), ctx)
.await
.map_err(BoxedError::new)
.context(ExternalSnafu)?;
match resp.data {
common_query::OutputData::AffectedRows(rows) => {
Ok(rows.try_into().map_err(|_| {
UnexpectedSnafu {
reason: format!("Failed to convert rows to u32: {}", rows),
}
.build()
})?)
}
_ => UnexpectedSnafu {
reason: "Unexpected output data",
}
.fail(),
}
}
}
}
}
}
/// Describe a peer of frontend
#[derive(Debug, Default)]
pub(crate) enum PeerDesc {
/// Distributed mode's frontend peer address
Dist {
/// frontend peer address
peer: Peer,
},
/// Standalone mode
#[default]
Standalone,
}
impl std::fmt::Display for PeerDesc {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
match self {
PeerDesc::Dist { peer } => write!(f, "{}", peer.addr),
PeerDesc::Standalone => write!(f, "standalone"),
}
}
}

View File

@@ -22,13 +22,14 @@ use common_telemetry::tracing::warn;
use common_time::Timestamp;
use datatypes::value::Value;
use session::context::QueryContextRef;
use snafu::ResultExt;
use snafu::{OptionExt, ResultExt};
use tokio::sync::oneshot;
use tokio::time::Instant;
use crate::batching_mode::task::BatchingTask;
use crate::batching_mode::time_window::TimeWindowExpr;
use crate::batching_mode::MIN_REFRESH_DURATION;
use crate::error::{DatatypesSnafu, InternalSnafu, TimeSnafu};
use crate::error::{DatatypesSnafu, InternalSnafu, TimeSnafu, UnexpectedSnafu};
use crate::{Error, FlowId};
/// The state of the [`BatchingTask`].
@@ -46,6 +47,8 @@ pub struct TaskState {
exec_state: ExecState,
/// Shutdown receiver
pub(crate) shutdown_rx: oneshot::Receiver<()>,
/// Task handle
pub(crate) task_handle: Option<tokio::task::JoinHandle<()>>,
}
impl TaskState {
pub fn new(query_ctx: QueryContextRef, shutdown_rx: oneshot::Receiver<()>) -> Self {
@@ -56,6 +59,7 @@ impl TaskState {
dirty_time_windows: Default::default(),
exec_state: ExecState::Idle,
shutdown_rx,
task_handle: None,
}
}
@@ -67,19 +71,44 @@ impl TaskState {
self.last_update_time = Instant::now();
}
/// wait for at least `last_query_duration`, at most `max_timeout` to start next query
/// Compute the next query delay based on the time window size or the last query duration.
/// Aiming to avoid too frequent queries. But also not too long delay.
/// The delay is computed as follows:
/// - If `time_window_size` is set, the delay is half the time window size, constrained to be
/// at least `last_query_duration` and at most `max_timeout`.
/// - If `time_window_size` is not set, the delay defaults to `last_query_duration`, constrained
/// to be at least `MIN_REFRESH_DURATION` and at most `max_timeout`.
///
/// if have more dirty time window, exec next query immediately
pub fn get_next_start_query_time(&self, max_timeout: Option<Duration>) -> Instant {
let next_duration = max_timeout
/// If there are dirty time windows, the function returns an immediate execution time to clean them.
/// TODO: Make this behavior configurable.
pub fn get_next_start_query_time(
&self,
flow_id: FlowId,
time_window_size: &Option<Duration>,
max_timeout: Option<Duration>,
) -> Instant {
let last_duration = max_timeout
.unwrap_or(self.last_query_duration)
.min(self.last_query_duration);
let next_duration = next_duration.max(MIN_REFRESH_DURATION);
.min(self.last_query_duration)
.max(MIN_REFRESH_DURATION);
let next_duration = time_window_size
.map(|t| {
let half = t / 2;
half.max(last_duration)
})
.unwrap_or(last_duration);
// if have dirty time window, execute immediately to clean dirty time window
if self.dirty_time_windows.windows.is_empty() {
self.last_update_time + next_duration
} else {
debug!(
"Flow id = {}, still have {} dirty time window({:?}), execute immediately",
flow_id,
self.dirty_time_windows.windows.len(),
self.dirty_time_windows.windows
);
Instant::now()
}
}
@@ -115,6 +144,15 @@ impl DirtyTimeWindows {
}
}
pub fn add_window(&mut self, start: Timestamp, end: Option<Timestamp>) {
self.windows.insert(start, end);
}
/// Clean all dirty time windows, useful when can't found time window expr
pub fn clean(&mut self) {
self.windows.clear();
}
/// Generate all filter expressions consuming all time windows
pub fn gen_filter_exprs(
&mut self,
@@ -177,6 +215,18 @@ impl DirtyTimeWindows {
let mut expr_lst = vec![];
for (start, end) in first_nth.into_iter() {
// align using time window exprs
let (start, end) = if let Some(ctx) = task_ctx {
let Some(time_window_expr) = &ctx.config.time_window_expr else {
UnexpectedSnafu {
reason: "time_window_expr is not set",
}
.fail()?
};
self.align_time_window(start, end, time_window_expr)?
} else {
(start, end)
};
debug!(
"Time window start: {:?}, end: {:?}",
start.to_iso8601_string(),
@@ -199,6 +249,30 @@ impl DirtyTimeWindows {
Ok(expr)
}
fn align_time_window(
&self,
start: Timestamp,
end: Option<Timestamp>,
time_window_expr: &TimeWindowExpr,
) -> Result<(Timestamp, Option<Timestamp>), Error> {
let align_start = time_window_expr.eval(start)?.0.context(UnexpectedSnafu {
reason: format!(
"Failed to align start time {:?} with time window expr {:?}",
start, time_window_expr
),
})?;
let align_end = end
.and_then(|end| {
time_window_expr
.eval(end)
// if after aligned, end is the same, then use end(because it's already aligned) else use aligned end
.map(|r| if r.0 == Some(end) { r.0 } else { r.1 })
.transpose()
})
.transpose()?;
Ok((align_start, align_end))
}
/// Merge time windows that overlaps or get too close
pub fn merge_dirty_time_windows(
&mut self,
@@ -287,8 +361,12 @@ enum ExecState {
#[cfg(test)]
mod test {
use pretty_assertions::assert_eq;
use session::context::QueryContext;
use super::*;
use crate::batching_mode::time_window::find_time_window_expr;
use crate::batching_mode::utils::sql_to_df_plan;
use crate::test_utils::create_test_query_engine;
#[test]
fn test_merge_dirty_time_windows() {
@@ -404,4 +482,59 @@ mod test {
assert_eq!(expected_filter_expr, to_sql.as_deref());
}
}
#[tokio::test]
async fn test_align_time_window() {
type TimeWindow = (Timestamp, Option<Timestamp>);
struct TestCase {
sql: String,
aligns: Vec<(TimeWindow, TimeWindow)>,
}
let testcases: Vec<TestCase> = vec![TestCase{
sql: "SELECT date_bin(INTERVAL '5 second', ts) AS time_window FROM numbers_with_ts GROUP BY time_window;".to_string(),
aligns: vec![
((Timestamp::new_second(3), None), (Timestamp::new_second(0), None)),
((Timestamp::new_second(8), None), (Timestamp::new_second(5), None)),
((Timestamp::new_second(8), Some(Timestamp::new_second(10))), (Timestamp::new_second(5), Some(Timestamp::new_second(10)))),
((Timestamp::new_second(8), Some(Timestamp::new_second(9))), (Timestamp::new_second(5), Some(Timestamp::new_second(10)))),
],
}];
let query_engine = create_test_query_engine();
let ctx = QueryContext::arc();
for TestCase { sql, aligns } in testcases {
let plan = sql_to_df_plan(ctx.clone(), query_engine.clone(), &sql, true)
.await
.unwrap();
let (column_name, time_window_expr, _, df_schema) = find_time_window_expr(
&plan,
query_engine.engine_state().catalog_manager().clone(),
ctx.clone(),
)
.await
.unwrap();
let time_window_expr = time_window_expr
.map(|expr| {
TimeWindowExpr::from_expr(
&expr,
&column_name,
&df_schema,
&query_engine.engine_state().session_state(),
)
})
.transpose()
.unwrap()
.unwrap();
let dirty = DirtyTimeWindows::default();
for (before_align, expected_after_align) in aligns {
let after_align = dirty
.align_time_window(before_align.0, before_align.1, &time_window_expr)
.unwrap();
assert_eq!(expected_after_align, after_align);
}
}
}
}

View File

@@ -12,33 +12,32 @@
// See the License for the specific language governing permissions and
// limitations under the License.
use std::collections::HashSet;
use std::collections::{BTreeSet, HashSet};
use std::ops::Deref;
use std::sync::{Arc, RwLock};
use std::time::{Duration, SystemTime, UNIX_EPOCH};
use api::v1::CreateTableExpr;
use arrow_schema::Fields;
use catalog::CatalogManagerRef;
use common_error::ext::BoxedError;
use common_meta::key::table_name::TableNameKey;
use common_meta::key::TableMetadataManagerRef;
use common_query::logical_plan::breakup_insert_plan;
use common_telemetry::tracing::warn;
use common_telemetry::{debug, info};
use common_time::Timestamp;
use datafusion::optimizer::analyzer::count_wildcard_rule::CountWildcardRule;
use datafusion::optimizer::AnalyzerRule;
use datafusion::sql::unparser::expr_to_sql;
use datafusion_common::tree_node::TreeNode;
use datafusion_common::tree_node::{Transformed, TreeNode};
use datafusion_expr::{DmlStatement, LogicalPlan, WriteOp};
use datatypes::prelude::ConcreteDataType;
use datatypes::schema::constraint::NOW_FN;
use datatypes::schema::{ColumnDefaultConstraint, ColumnSchema};
use datatypes::value::Value;
use datatypes::schema::{ColumnSchema, Schema};
use operator::expr_helper::column_schemas_to_defs;
use query::query_engine::DefaultSerializer;
use query::QueryEngineRef;
use session::context::QueryContextRef;
use snafu::{OptionExt, ResultExt};
use snafu::{ensure, OptionExt, ResultExt};
use substrait::{DFLogicalSubstraitConvertor, SubstraitPlan};
use table::metadata::RawTableMeta;
use tokio::sync::oneshot;
use tokio::sync::oneshot::error::TryRecvError;
use tokio::time::Instant;
@@ -48,14 +47,16 @@ use crate::batching_mode::frontend_client::FrontendClient;
use crate::batching_mode::state::TaskState;
use crate::batching_mode::time_window::TimeWindowExpr;
use crate::batching_mode::utils::{
sql_to_df_plan, AddAutoColumnRewriter, AddFilterRewriter, FindGroupByFinalName,
get_table_info_df_schema, sql_to_df_plan, AddAutoColumnRewriter, AddFilterRewriter,
FindGroupByFinalName,
};
use crate::batching_mode::{
DEFAULT_BATCHING_ENGINE_QUERY_TIMEOUT, MIN_REFRESH_DURATION, SLOW_QUERY_THRESHOLD,
};
use crate::df_optimizer::apply_df_optimizer;
use crate::error::{
ConvertColumnSchemaSnafu, DatafusionSnafu, DatatypesSnafu, ExternalSnafu, InvalidRequestSnafu,
SubstraitEncodeLogicalPlanSnafu, TableNotFoundMetaSnafu, TableNotFoundSnafu, UnexpectedSnafu,
ConvertColumnSchemaSnafu, DatafusionSnafu, ExternalSnafu, InvalidQuerySnafu,
SubstraitEncodeLogicalPlanSnafu, UnexpectedSnafu,
};
use crate::metrics::{
METRIC_FLOW_BATCHING_ENGINE_QUERY_TIME, METRIC_FLOW_BATCHING_ENGINE_SLOW_QUERY,
@@ -73,7 +74,7 @@ pub struct TaskConfig {
pub expire_after: Option<i64>,
sink_table_name: [String; 3],
pub source_table_names: HashSet<[String; 3]>,
table_meta: TableMetadataManagerRef,
catalog_manager: CatalogManagerRef,
}
#[derive(Clone)]
@@ -93,7 +94,7 @@ impl BatchingTask {
sink_table_name: [String; 3],
source_table_names: Vec<[String; 3]>,
query_ctx: QueryContextRef,
table_meta: TableMetadataManagerRef,
catalog_manager: CatalogManagerRef,
shutdown_rx: oneshot::Receiver<()>,
) -> Self {
Self {
@@ -105,32 +106,48 @@ impl BatchingTask {
expire_after,
sink_table_name,
source_table_names: source_table_names.into_iter().collect(),
table_meta,
catalog_manager,
}),
state: Arc::new(RwLock::new(TaskState::new(query_ctx, shutdown_rx))),
}
}
/// Test execute, for check syntax or such
pub async fn check_execute(
&self,
engine: &QueryEngineRef,
frontend_client: &Arc<FrontendClient>,
) -> Result<Option<(u32, Duration)>, Error> {
// use current time to test get a dirty time window, which should be safe
let start = SystemTime::now();
let ts = Timestamp::new_second(
start
.duration_since(UNIX_EPOCH)
/// mark time window range (now - expire_after, now) as dirty (or (0, now) if expire_after not set)
///
/// useful for flush_flow to flush dirty time windows range
pub fn mark_all_windows_as_dirty(&self) -> Result<(), Error> {
let now = SystemTime::now();
let now = Timestamp::new_second(
now.duration_since(UNIX_EPOCH)
.expect("Time went backwards")
.as_secs() as _,
);
let lower_bound = self
.config
.expire_after
.map(|e| now.sub_duration(Duration::from_secs(e as _)))
.transpose()
.map_err(BoxedError::new)
.context(ExternalSnafu)?
.unwrap_or(Timestamp::new_second(0));
debug!(
"Flow {} mark range ({:?}, {:?}) as dirty",
self.config.flow_id, lower_bound, now
);
self.state
.write()
.unwrap()
.dirty_time_windows
.add_lower_bounds(vec![ts].into_iter());
.add_window(lower_bound, Some(now));
Ok(())
}
/// Create sink table if not exists
pub async fn check_or_create_sink_table(
&self,
engine: &QueryEngineRef,
frontend_client: &Arc<FrontendClient>,
) -> Result<Option<(u32, Duration)>, Error> {
if !self.is_table_exist(&self.config.sink_table_name).await? {
let create_table = self.gen_create_table_expr(engine.clone()).await?;
info!(
@@ -143,18 +160,14 @@ impl BatchingTask {
self.config.sink_table_name.join(".")
);
}
self.gen_exec_once(engine, frontend_client).await
Ok(None)
}
async fn is_table_exist(&self, table_name: &[String; 3]) -> Result<bool, Error> {
self.config
.table_meta
.table_name_manager()
.exists(TableNameKey {
catalog: &table_name[0],
schema: &table_name[1],
table: &table_name[2],
})
.catalog_manager
.table_exists(&table_name[0], &table_name[1], &table_name[2], None)
.await
.map_err(BoxedError::new)
.context(ExternalSnafu)
@@ -166,8 +179,10 @@ impl BatchingTask {
frontend_client: &Arc<FrontendClient>,
) -> Result<Option<(u32, Duration)>, Error> {
if let Some(new_query) = self.gen_insert_plan(engine).await? {
debug!("Generate new query: {}", new_query);
self.execute_logical_plan(frontend_client, &new_query).await
} else {
debug!("Generate no query");
Ok(None)
}
}
@@ -176,67 +191,35 @@ impl BatchingTask {
&self,
engine: &QueryEngineRef,
) -> Result<Option<LogicalPlan>, Error> {
let full_table_name = self.config.sink_table_name.clone().join(".");
let table_id = self
.config
.table_meta
.table_name_manager()
.get(common_meta::key::table_name::TableNameKey::new(
&self.config.sink_table_name[0],
&self.config.sink_table_name[1],
&self.config.sink_table_name[2],
))
.await
.with_context(|_| TableNotFoundMetaSnafu {
msg: full_table_name.clone(),
})?
.map(|t| t.table_id())
.with_context(|| TableNotFoundSnafu {
name: full_table_name.clone(),
})?;
let table = self
.config
.table_meta
.table_info_manager()
.get(table_id)
.await
.with_context(|_| TableNotFoundMetaSnafu {
msg: full_table_name.clone(),
})?
.with_context(|| TableNotFoundSnafu {
name: full_table_name.clone(),
})?
.into_inner();
let schema: datatypes::schema::Schema = table
.table_info
.meta
.schema
.clone()
.try_into()
.with_context(|_| DatatypesSnafu {
extra: format!(
"Failed to convert schema from raw schema, raw_schema={:?}",
table.table_info.meta.schema
),
})?;
let df_schema = Arc::new(schema.arrow_schema().clone().try_into().with_context(|_| {
DatafusionSnafu {
context: format!(
"Failed to convert arrow schema to datafusion schema, arrow_schema={:?}",
schema.arrow_schema()
),
}
})?);
let (table, df_schema) = get_table_info_df_schema(
self.config.catalog_manager.clone(),
self.config.sink_table_name.clone(),
)
.await?;
let new_query = self
.gen_query_with_time_window(engine.clone(), &table.table_info.meta)
.gen_query_with_time_window(engine.clone(), &table.meta.schema)
.await?;
let insert_into = if let Some((new_query, _column_cnt)) = new_query {
// first check if all columns in input query exists in sink table
// since insert into ref to names in record batch generate by given query
let table_columns = df_schema
.columns()
.into_iter()
.map(|c| c.name)
.collect::<BTreeSet<_>>();
for column in new_query.schema().columns() {
ensure!(
table_columns.contains(column.name()),
InvalidQuerySnafu {
reason: format!(
"Column {} not found in sink table with columns {:?}",
column, table_columns
),
}
);
}
// update_at& time index placeholder (if exists) should have default value
LogicalPlan::Dml(DmlStatement::new(
datafusion_common::TableReference::Full {
@@ -251,6 +234,9 @@ impl BatchingTask {
} else {
return Ok(None);
};
let insert_into = insert_into.recompute_schema().context(DatafusionSnafu {
context: "Failed to recompute schema",
})?;
Ok(Some(insert_into))
}
@@ -259,14 +245,11 @@ impl BatchingTask {
frontend_client: &Arc<FrontendClient>,
expr: CreateTableExpr,
) -> Result<(), Error> {
let db_client = frontend_client.get_database_client().await?;
db_client
.database
.create(expr.clone())
.await
.with_context(|_| InvalidRequestSnafu {
context: format!("Failed to create table with expr: {:?}", expr),
})?;
let catalog = &self.config.sink_table_name[0];
let schema = &self.config.sink_table_name[1];
frontend_client
.create(expr.clone(), catalog, schema)
.await?;
Ok(())
}
@@ -277,27 +260,78 @@ impl BatchingTask {
) -> Result<Option<(u32, Duration)>, Error> {
let instant = Instant::now();
let flow_id = self.config.flow_id;
let db_client = frontend_client.get_database_client().await?;
let peer_addr = db_client.peer.addr;
debug!(
"Executing flow {flow_id}(expire_after={:?} secs) on {:?} with query {}",
self.config.expire_after, peer_addr, &plan
"Executing flow {flow_id}(expire_after={:?} secs) with query {}",
self.config.expire_after, &plan
);
let timer = METRIC_FLOW_BATCHING_ENGINE_QUERY_TIME
.with_label_values(&[flow_id.to_string().as_str()])
.start_timer();
let catalog = &self.config.sink_table_name[0];
let schema = &self.config.sink_table_name[1];
let message = DFLogicalSubstraitConvertor {}
.encode(plan, DefaultSerializer)
.context(SubstraitEncodeLogicalPlanSnafu)?;
// fix all table ref by make it fully qualified, i.e. "table_name" => "catalog_name.schema_name.table_name"
let fixed_plan = plan
.clone()
.transform_down_with_subqueries(|p| {
if let LogicalPlan::TableScan(mut table_scan) = p {
let resolved = table_scan.table_name.resolve(catalog, schema);
table_scan.table_name = resolved.into();
Ok(Transformed::yes(LogicalPlan::TableScan(table_scan)))
} else {
Ok(Transformed::no(p))
}
})
.with_context(|_| DatafusionSnafu {
context: format!("Failed to fix table ref in logical plan, plan={:?}", plan),
})?
.data;
let req = api::v1::greptime_request::Request::Query(api::v1::QueryRequest {
query: Some(api::v1::query_request::Query::LogicalPlan(message.to_vec())),
});
let expanded_plan = CountWildcardRule::new()
.analyze(fixed_plan.clone(), &Default::default())
.with_context(|_| DatafusionSnafu {
context: format!(
"Failed to expand wildcard in logical plan, plan={:?}",
fixed_plan
),
})?;
let res = db_client.database.handle(req).await;
drop(timer);
let plan = expanded_plan;
let mut peer_desc = None;
let res = {
let _timer = METRIC_FLOW_BATCHING_ENGINE_QUERY_TIME
.with_label_values(&[flow_id.to_string().as_str()])
.start_timer();
// hack and special handling the insert logical plan
let req = if let Some((insert_to, insert_plan)) =
breakup_insert_plan(&plan, catalog, schema)
{
let message = DFLogicalSubstraitConvertor {}
.encode(&insert_plan, DefaultSerializer)
.context(SubstraitEncodeLogicalPlanSnafu)?;
api::v1::greptime_request::Request::Query(api::v1::QueryRequest {
query: Some(api::v1::query_request::Query::InsertIntoPlan(
api::v1::InsertIntoPlan {
table_name: Some(insert_to),
logical_plan: message.to_vec(),
},
)),
})
} else {
let message = DFLogicalSubstraitConvertor {}
.encode(&plan, DefaultSerializer)
.context(SubstraitEncodeLogicalPlanSnafu)?;
api::v1::greptime_request::Request::Query(api::v1::QueryRequest {
query: Some(api::v1::query_request::Query::LogicalPlan(message.to_vec())),
})
};
frontend_client
.handle(req, catalog, schema, &mut peer_desc)
.await
};
let elapsed = instant.elapsed();
if let Ok(affected_rows) = &res {
@@ -307,19 +341,23 @@ impl BatchingTask {
);
} else if let Err(err) = &res {
warn!(
"Failed to execute Flow {flow_id} on frontend {}, result: {err:?}, elapsed: {:?} with query: {}",
peer_addr, elapsed, &plan
"Failed to execute Flow {flow_id} on frontend {:?}, result: {err:?}, elapsed: {:?} with query: {}",
peer_desc, elapsed, &plan
);
}
// record slow query
if elapsed >= SLOW_QUERY_THRESHOLD {
warn!(
"Flow {flow_id} on frontend {} executed for {:?} before complete, query: {}",
peer_addr, elapsed, &plan
"Flow {flow_id} on frontend {:?} executed for {:?} before complete, query: {}",
peer_desc, elapsed, &plan
);
METRIC_FLOW_BATCHING_ENGINE_SLOW_QUERY
.with_label_values(&[flow_id.to_string().as_str(), &plan.to_string(), &peer_addr])
.with_label_values(&[
flow_id.to_string().as_str(),
&plan.to_string(),
&peer_desc.unwrap_or_default().to_string(),
])
.observe(elapsed.as_secs_f64());
}
@@ -328,12 +366,7 @@ impl BatchingTask {
.unwrap()
.after_query_exec(elapsed, res.is_ok());
let res = res.context(InvalidRequestSnafu {
context: format!(
"Failed to execute query for flow={}: \'{}\'",
self.config.flow_id, &plan
),
})?;
let res = res?;
Ok(Some((res, elapsed)))
}
@@ -347,6 +380,23 @@ impl BatchingTask {
frontend_client: Arc<FrontendClient>,
) {
loop {
// first check if shutdown signal is received
// if so, break the loop
{
let mut state = self.state.write().unwrap();
match state.shutdown_rx.try_recv() {
Ok(()) => break,
Err(TryRecvError::Closed) => {
warn!(
"Unexpected shutdown flow {}, shutdown anyway",
self.config.flow_id
);
break;
}
Err(TryRecvError::Empty) => (),
}
}
let mut new_query = None;
let mut gen_and_exec = async || {
new_query = self.gen_insert_plan(&engine).await?;
@@ -360,19 +410,17 @@ impl BatchingTask {
// normal execute, sleep for some time before doing next query
Ok(Some(_)) => {
let sleep_until = {
let mut state = self.state.write().unwrap();
match state.shutdown_rx.try_recv() {
Ok(()) => break,
Err(TryRecvError::Closed) => {
warn!(
"Unexpected shutdown flow {}, shutdown anyway",
self.config.flow_id
);
break;
}
Err(TryRecvError::Empty) => (),
}
state.get_next_start_query_time(Some(DEFAULT_BATCHING_ENGINE_QUERY_TIMEOUT))
let state = self.state.write().unwrap();
state.get_next_start_query_time(
self.config.flow_id,
&self
.config
.time_window_expr
.as_ref()
.and_then(|t| *t.time_window_size()),
Some(DEFAULT_BATCHING_ENGINE_QUERY_TIMEOUT),
)
};
tokio::time::sleep_until(sleep_until).await;
}
@@ -386,14 +434,18 @@ impl BatchingTask {
continue;
}
// TODO(discord9): this error should have better place to go, but for now just print error, also more context is needed
Err(err) => match new_query {
Some(query) => {
common_telemetry::error!(err; "Failed to execute query for flow={} with query: {query}", self.config.flow_id)
Err(err) => {
match new_query {
Some(query) => {
common_telemetry::error!(err; "Failed to execute query for flow={} with query: {query}", self.config.flow_id)
}
None => {
common_telemetry::error!(err; "Failed to generate query for flow={}", self.config.flow_id)
}
}
None => {
common_telemetry::error!(err; "Failed to generate query for flow={}", self.config.flow_id)
}
},
// also sleep for a little while before try again to prevent flooding logs
tokio::time::sleep(MIN_REFRESH_DURATION).await;
}
}
}
}
@@ -418,7 +470,7 @@ impl BatchingTask {
async fn gen_query_with_time_window(
&self,
engine: QueryEngineRef,
sink_table_meta: &RawTableMeta,
sink_table_schema: &Arc<Schema>,
) -> Result<Option<(LogicalPlan, usize)>, Error> {
let query_ctx = self.state.read().unwrap().query_ctx.clone();
let start = SystemTime::now();
@@ -477,9 +529,11 @@ impl BatchingTask {
debug!(
"Flow id = {:?}, can't get window size: precise_lower_bound={expire_time_window_bound:?}, using the same query", self.config.flow_id
);
// clean dirty time window too, this could be from create flow's check_execute
self.state.write().unwrap().dirty_time_windows.clean();
let mut add_auto_column =
AddAutoColumnRewriter::new(sink_table_meta.schema.clone());
AddAutoColumnRewriter::new(sink_table_schema.clone());
let plan = self
.config
.plan
@@ -487,7 +541,10 @@ impl BatchingTask {
.clone()
.rewrite(&mut add_auto_column)
.with_context(|_| DatafusionSnafu {
context: format!("Failed to rewrite plan {:?}", self.config.plan),
context: format!(
"Failed to rewrite plan:\n {}\n",
self.config.plan
),
})?
.data;
let schema_len = plan.schema().fields().len();
@@ -515,18 +572,23 @@ impl BatchingTask {
return Ok(None);
};
// TODO(discord9): add auto column or not? This might break compatibility for auto created sink table before this, but that's ok right?
let mut add_filter = AddFilterRewriter::new(expr);
let mut add_auto_column = AddAutoColumnRewriter::new(sink_table_meta.schema.clone());
// make a not optimized plan for clearer unparse
let mut add_auto_column = AddAutoColumnRewriter::new(sink_table_schema.clone());
let plan = sql_to_df_plan(query_ctx.clone(), engine.clone(), &self.config.query, false)
.await?;
plan.clone()
let rewrite = plan
.clone()
.rewrite(&mut add_filter)
.and_then(|p| p.data.rewrite(&mut add_auto_column))
.with_context(|_| DatafusionSnafu {
context: format!("Failed to rewrite plan {plan:?}"),
context: format!("Failed to rewrite plan:\n {}\n", plan),
})?
.data
.data;
// only apply optimize after complex rewrite is done
apply_df_optimizer(rewrite).await?
};
Ok(Some((new_plan, schema_len)))
@@ -534,7 +596,7 @@ impl BatchingTask {
}
// auto created table have a auto added column `update_at`, and optional have a `AUTO_CREATED_PLACEHOLDER_TS_COL` column for time index placeholder if no timestamp column is specified
// TODO(discord9): unit test
// TODO(discord9): for now no default value is set for auto added column for compatibility reason with streaming mode, but this might change in favor of simpler code?
fn create_table_with_expr(
plan: &LogicalPlan,
sink_table_name: &[String; 3],
@@ -558,11 +620,7 @@ fn create_table_with_expr(
AUTO_CREATED_UPDATE_AT_TS_COL,
ConcreteDataType::timestamp_millisecond_datatype(),
true,
)
.with_default_constraint(Some(ColumnDefaultConstraint::Function(NOW_FN.to_string())))
.context(DatatypesSnafu {
extra: "Failed to build column `update_at TimestampMillisecond default now()`",
})?;
);
column_schemas.push(update_at_schema);
let time_index = if let Some(time_index) = first_time_stamp {
@@ -574,16 +632,7 @@ fn create_table_with_expr(
ConcreteDataType::timestamp_millisecond_datatype(),
false,
)
.with_time_index(true)
.with_default_constraint(Some(ColumnDefaultConstraint::Value(Value::Timestamp(
Timestamp::new_millisecond(0),
))))
.context(DatatypesSnafu {
extra: format!(
"Failed to build column `{} TimestampMillisecond TIME INDEX default 0`",
AUTO_CREATED_PLACEHOLDER_TS_COL
),
})?,
.with_time_index(true),
);
AUTO_CREATED_PLACEHOLDER_TS_COL.to_string()
};
@@ -675,20 +724,14 @@ mod test {
AUTO_CREATED_UPDATE_AT_TS_COL,
ConcreteDataType::timestamp_millisecond_datatype(),
true,
)
.with_default_constraint(Some(ColumnDefaultConstraint::Function(NOW_FN.to_string())))
.unwrap();
);
let ts_placeholder_schema = ColumnSchema::new(
AUTO_CREATED_PLACEHOLDER_TS_COL,
ConcreteDataType::timestamp_millisecond_datatype(),
false,
)
.with_time_index(true)
.with_default_constraint(Some(ColumnDefaultConstraint::Value(Value::Timestamp(
Timestamp::new_millisecond(0),
))))
.unwrap();
.with_time_index(true);
let testcases = vec![
TestCase {

View File

@@ -55,6 +55,9 @@ use crate::error::{
use crate::expr::error::DataTypeSnafu;
use crate::Error;
/// Represents a test timestamp in seconds since the Unix epoch.
const DEFAULT_TEST_TIMESTAMP: Timestamp = Timestamp::new_second(17_0000_0000);
/// Time window expr like `date_bin(INTERVAL '1' MINUTE, ts)`, this type help with
/// evaluating the expr using given timestamp
///
@@ -70,9 +73,26 @@ pub struct TimeWindowExpr {
pub column_name: String,
logical_expr: Expr,
df_schema: DFSchema,
eval_time_window_size: Option<std::time::Duration>,
}
impl std::fmt::Display for TimeWindowExpr {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("TimeWindowExpr")
.field("phy_expr", &self.phy_expr.to_string())
.field("column_name", &self.column_name)
.field("logical_expr", &self.logical_expr.to_string())
.field("df_schema", &self.df_schema)
.finish()
}
}
impl TimeWindowExpr {
/// The time window size of the expr, get from calling `eval` with a test timestamp
pub fn time_window_size(&self) -> &Option<std::time::Duration> {
&self.eval_time_window_size
}
pub fn from_expr(
expr: &Expr,
column_name: &str,
@@ -80,12 +100,28 @@ impl TimeWindowExpr {
session: &SessionState,
) -> Result<Self, Error> {
let phy_expr: PhysicalExprRef = to_phy_expr(expr, df_schema, session)?;
Ok(Self {
let mut zelf = Self {
phy_expr,
column_name: column_name.to_string(),
logical_expr: expr.clone(),
df_schema: df_schema.clone(),
})
eval_time_window_size: None,
};
let test_ts = DEFAULT_TEST_TIMESTAMP;
let (l, u) = zelf.eval(test_ts)?;
let time_window_size = match (l, u) {
(Some(l), Some(u)) => u.sub(&l).map(|r| r.to_std()).transpose().map_err(|_| {
UnexpectedSnafu {
reason: format!(
"Expect upper bound older than lower bound, found upper={u:?} and lower={l:?}"
),
}
.build()
})?,
_ => None,
};
zelf.eval_time_window_size = time_window_size;
Ok(zelf)
}
pub fn eval(
@@ -256,7 +292,7 @@ fn columnar_to_ts_vector(columnar: &ColumnarValue) -> Result<Vec<Option<Timestam
Ok(val)
}
/// Return (the column name of time index column, the time window expr, the expected time unit of time index column, the expr's schema for evaluating the time window)
/// Return (`the column name of time index column`, `the time window expr`, `the expected time unit of time index column`, `the expr's schema for evaluating the time window`)
///
/// The time window expr is expected to have one input column with Timestamp type, and also return Timestamp type, the time window expr is expected
/// to be monotonic increasing and appears in the innermost GROUP BY clause
@@ -693,6 +729,28 @@ mod test {
),
"SELECT arrow_cast(date_bin(INTERVAL '1 MINS', numbers_with_ts.ts), 'Timestamp(Second, None)') AS time_window FROM numbers_with_ts WHERE ((ts >= CAST('2025-02-24 10:48:00' AS TIMESTAMP)) AND (ts <= CAST('2025-02-24 10:49:00' AS TIMESTAMP))) GROUP BY arrow_cast(date_bin(INTERVAL '1 MINS', numbers_with_ts.ts), 'Timestamp(Second, None)')"
),
// complex time window index with where
(
"SELECT arrow_cast(date_bin(INTERVAL '1 MINS', numbers_with_ts.ts), 'Timestamp(Second, None)') AS time_window FROM numbers_with_ts WHERE number in (2, 3, 4) GROUP BY time_window;",
Timestamp::new(1740394109, TimeUnit::Second),
(
"ts".to_string(),
Some(Timestamp::new(1740394080, TimeUnit::Second)),
Some(Timestamp::new(1740394140, TimeUnit::Second)),
),
"SELECT arrow_cast(date_bin(INTERVAL '1 MINS', numbers_with_ts.ts), 'Timestamp(Second, None)') AS time_window FROM numbers_with_ts WHERE numbers_with_ts.number IN (2, 3, 4) AND ((ts >= CAST('2025-02-24 10:48:00' AS TIMESTAMP)) AND (ts <= CAST('2025-02-24 10:49:00' AS TIMESTAMP))) GROUP BY arrow_cast(date_bin(INTERVAL '1 MINS', numbers_with_ts.ts), 'Timestamp(Second, None)')"
),
// complex time window index with between and
(
"SELECT arrow_cast(date_bin(INTERVAL '1 MINS', numbers_with_ts.ts), 'Timestamp(Second, None)') AS time_window FROM numbers_with_ts WHERE number BETWEEN 2 AND 4 GROUP BY time_window;",
Timestamp::new(1740394109, TimeUnit::Second),
(
"ts".to_string(),
Some(Timestamp::new(1740394080, TimeUnit::Second)),
Some(Timestamp::new(1740394140, TimeUnit::Second)),
),
"SELECT arrow_cast(date_bin(INTERVAL '1 MINS', numbers_with_ts.ts), 'Timestamp(Second, None)') AS time_window FROM numbers_with_ts WHERE (numbers_with_ts.number BETWEEN 2 AND 4) AND ((ts >= CAST('2025-02-24 10:48:00' AS TIMESTAMP)) AND (ts <= CAST('2025-02-24 10:49:00' AS TIMESTAMP))) GROUP BY arrow_cast(date_bin(INTERVAL '1 MINS', numbers_with_ts.ts), 'Timestamp(Second, None)')"
),
// no time index
(
"SELECT date_bin('5 minutes', ts) FROM numbers_with_ts;",

View File

@@ -14,29 +14,63 @@
//! some utils for helping with batching mode
use std::collections::HashSet;
use std::collections::{BTreeSet, HashSet};
use std::sync::Arc;
use catalog::CatalogManagerRef;
use common_error::ext::BoxedError;
use common_telemetry::{debug, info};
use common_telemetry::debug;
use datafusion::error::Result as DfResult;
use datafusion::logical_expr::Expr;
use datafusion::sql::unparser::Unparser;
use datafusion_common::tree_node::{
Transformed, TreeNodeRecursion, TreeNodeRewriter, TreeNodeVisitor,
};
use datafusion_common::DataFusionError;
use datafusion_expr::{Distinct, LogicalPlan};
use datatypes::schema::RawSchema;
use datafusion_common::{DFSchema, DataFusionError, ScalarValue};
use datafusion_expr::{Distinct, LogicalPlan, Projection};
use datatypes::schema::SchemaRef;
use query::parser::QueryLanguageParser;
use query::QueryEngineRef;
use session::context::QueryContextRef;
use snafu::ResultExt;
use snafu::{OptionExt, ResultExt};
use table::metadata::TableInfo;
use crate::adapter::AUTO_CREATED_PLACEHOLDER_TS_COL;
use crate::df_optimizer::apply_df_optimizer;
use crate::error::{DatafusionSnafu, ExternalSnafu};
use crate::Error;
use crate::error::{DatafusionSnafu, ExternalSnafu, TableNotFoundSnafu};
use crate::{Error, TableName};
pub async fn get_table_info_df_schema(
catalog_mr: CatalogManagerRef,
table_name: TableName,
) -> Result<(Arc<TableInfo>, Arc<DFSchema>), Error> {
let full_table_name = table_name.clone().join(".");
let table = catalog_mr
.table(&table_name[0], &table_name[1], &table_name[2], None)
.await
.map_err(BoxedError::new)
.context(ExternalSnafu)?
.context(TableNotFoundSnafu {
name: &full_table_name,
})?;
let table_info = table.table_info().clone();
let schema = table_info.meta.schema.clone();
let df_schema: Arc<DFSchema> = Arc::new(
schema
.arrow_schema()
.clone()
.try_into()
.with_context(|_| DatafusionSnafu {
context: format!(
"Failed to convert arrow schema to datafusion schema, arrow_schema={:?}",
schema.arrow_schema()
),
})?,
);
Ok((table_info, df_schema))
}
/// Convert sql to datafusion logical plan
pub async fn sql_to_df_plan(
@@ -104,9 +138,12 @@ impl TreeNodeVisitor<'_> for FindGroupByFinalName {
fn f_down(&mut self, node: &Self::Node) -> datafusion_common::Result<TreeNodeRecursion> {
if let LogicalPlan::Aggregate(aggregate) = node {
self.group_exprs = Some(aggregate.group_expr.iter().cloned().collect());
debug!("Group by exprs: {:?}", self.group_exprs);
debug!(
"FindGroupByFinalName: Get Group by exprs from Aggregate: {:?}",
self.group_exprs
);
} else if let LogicalPlan::Distinct(distinct) = node {
debug!("Distinct: {:#?}", distinct);
debug!("FindGroupByFinalName: Distinct: {}", node);
match distinct {
Distinct::All(input) => {
if let LogicalPlan::TableScan(table_scan) = &**input {
@@ -128,7 +165,10 @@ impl TreeNodeVisitor<'_> for FindGroupByFinalName {
self.group_exprs = Some(distinct_on.on_expr.iter().cloned().collect())
}
}
debug!("Group by exprs: {:?}", self.group_exprs);
debug!(
"FindGroupByFinalName: Get Group by exprs from Distinct: {:?}",
self.group_exprs
);
}
Ok(TreeNodeRecursion::Continue)
@@ -164,14 +204,16 @@ impl TreeNodeVisitor<'_> for FindGroupByFinalName {
/// (which doesn't necessary need to have exact name just need to be a extra timestamp column)
/// and `__ts_placeholder`(this column need to have exact this name and be a timestamp)
/// with values like `now()` and `0`
///
/// it also give existing columns alias to column in sink table if needed
#[derive(Debug)]
pub struct AddAutoColumnRewriter {
pub schema: RawSchema,
pub schema: SchemaRef,
pub is_rewritten: bool,
}
impl AddAutoColumnRewriter {
pub fn new(schema: RawSchema) -> Self {
pub fn new(schema: SchemaRef) -> Self {
Self {
schema,
is_rewritten: false,
@@ -181,37 +223,97 @@ impl AddAutoColumnRewriter {
impl TreeNodeRewriter for AddAutoColumnRewriter {
type Node = LogicalPlan;
fn f_down(&mut self, node: Self::Node) -> DfResult<Transformed<Self::Node>> {
fn f_down(&mut self, mut node: Self::Node) -> DfResult<Transformed<Self::Node>> {
if self.is_rewritten {
return Ok(Transformed::no(node));
}
// if is distinct all, go one level down
if let LogicalPlan::Distinct(Distinct::All(_)) = node {
return Ok(Transformed::no(node));
// if is distinct all, wrap it in a projection
if let LogicalPlan::Distinct(Distinct::All(_)) = &node {
let mut exprs = vec![];
for field in node.schema().fields().iter() {
exprs.push(Expr::Column(datafusion::common::Column::new_unqualified(
field.name(),
)));
}
let projection =
LogicalPlan::Projection(Projection::try_new(exprs, Arc::new(node.clone()))?);
node = projection;
}
// handle table_scan by wrap it in a projection
else if let LogicalPlan::TableScan(table_scan) = node {
let mut exprs = vec![];
for field in table_scan.projected_schema.fields().iter() {
exprs.push(Expr::Column(datafusion::common::Column::new(
Some(table_scan.table_name.clone()),
field.name(),
)));
}
let projection = LogicalPlan::Projection(Projection::try_new(
exprs,
Arc::new(LogicalPlan::TableScan(table_scan)),
)?);
node = projection;
}
// FIXME(discord9): just read plan.expr and do stuffs
let mut exprs = node.expressions();
// only do rewrite if found the outermost projection
let mut exprs = if let LogicalPlan::Projection(project) = &node {
project.expr.clone()
} else {
return Ok(Transformed::no(node));
};
let all_names = self
.schema
.column_schemas()
.iter()
.map(|c| c.name.clone())
.collect::<BTreeSet<_>>();
// first match by position
for (idx, expr) in exprs.iter_mut().enumerate() {
if !all_names.contains(&expr.qualified_name().1) {
if let Some(col_name) = self
.schema
.column_schemas()
.get(idx)
.map(|c| c.name.clone())
{
// if the data type mismatched, later check_execute will error out
// hence no need to check it here, beside, optimize pass might be able to cast it
// so checking here is not necessary
*expr = expr.clone().alias(col_name);
}
}
}
// add columns if have different column count
let query_col_cnt = exprs.len();
let table_col_cnt = self.schema.column_schemas.len();
info!("query_col_cnt={query_col_cnt}, table_col_cnt={table_col_cnt}");
let table_col_cnt = self.schema.column_schemas().len();
debug!("query_col_cnt={query_col_cnt}, table_col_cnt={table_col_cnt}");
let placeholder_ts_expr =
datafusion::logical_expr::lit(ScalarValue::TimestampMillisecond(Some(0), None))
.alias(AUTO_CREATED_PLACEHOLDER_TS_COL);
if query_col_cnt == table_col_cnt {
self.is_rewritten = true;
return Ok(Transformed::no(node));
// still need to add alias, see below
} else if query_col_cnt + 1 == table_col_cnt {
let last_col_schema = self.schema.column_schemas.last().unwrap();
let last_col_schema = self.schema.column_schemas().last().unwrap();
// if time index column is auto created add it
if last_col_schema.name == AUTO_CREATED_PLACEHOLDER_TS_COL
&& self.schema.timestamp_index == Some(table_col_cnt - 1)
&& self.schema.timestamp_index() == Some(table_col_cnt - 1)
{
exprs.push(datafusion::logical_expr::lit(0));
exprs.push(placeholder_ts_expr);
} else if last_col_schema.data_type.is_timestamp() {
// is the update at column
exprs.push(datafusion::prelude::now());
exprs.push(datafusion::prelude::now().alias(&last_col_schema.name));
} else {
// helpful error message
return Err(DataFusionError::Plan(format!(
@@ -221,11 +323,11 @@ impl TreeNodeRewriter for AddAutoColumnRewriter {
)));
}
} else if query_col_cnt + 2 == table_col_cnt {
let mut col_iter = self.schema.column_schemas.iter().rev();
let mut col_iter = self.schema.column_schemas().iter().rev();
let last_col_schema = col_iter.next().unwrap();
let second_last_col_schema = col_iter.next().unwrap();
if second_last_col_schema.data_type.is_timestamp() {
exprs.push(datafusion::prelude::now());
exprs.push(datafusion::prelude::now().alias(&second_last_col_schema.name));
} else {
return Err(DataFusionError::Plan(format!(
"Expect the second last column in the table to be timestamp column, found column {} with type {:?}",
@@ -235,9 +337,9 @@ impl TreeNodeRewriter for AddAutoColumnRewriter {
}
if last_col_schema.name == AUTO_CREATED_PLACEHOLDER_TS_COL
&& self.schema.timestamp_index == Some(table_col_cnt - 1)
&& self.schema.timestamp_index() == Some(table_col_cnt - 1)
{
exprs.push(datafusion::logical_expr::lit(0));
exprs.push(placeholder_ts_expr);
} else {
return Err(DataFusionError::Plan(format!(
"Expect timestamp column {}, found {:?}",
@@ -247,7 +349,7 @@ impl TreeNodeRewriter for AddAutoColumnRewriter {
} else {
return Err(DataFusionError::Plan(format!(
"Expect table have 0,1 or 2 columns more than query columns, found {} query columns {:?}, {} table columns {:?}",
query_col_cnt, node.expressions(), table_col_cnt, self.schema.column_schemas
query_col_cnt, exprs, table_col_cnt, self.schema.column_schemas()
)));
}
@@ -255,9 +357,12 @@ impl TreeNodeRewriter for AddAutoColumnRewriter {
let new_plan = node.with_new_exprs(exprs, node.inputs().into_iter().cloned().collect())?;
Ok(Transformed::yes(new_plan))
}
}
// TODO(discord9): a method to found out the precise time window
/// We might add new columns, so we need to recompute the schema
fn f_up(&mut self, node: Self::Node) -> DfResult<Transformed<Self::Node>> {
node.recompute_schema().map(Transformed::yes)
}
}
/// Find out the `Filter` Node corresponding to innermost(deepest) `WHERE` and add a new filter expr to it
#[derive(Debug)]
@@ -301,11 +406,15 @@ impl TreeNodeRewriter for AddFilterRewriter {
#[cfg(test)]
mod test {
use std::sync::Arc;
use datafusion_common::tree_node::TreeNode as _;
use datatypes::prelude::ConcreteDataType;
use datatypes::schema::ColumnSchema;
use datatypes::schema::{ColumnSchema, Schema};
use pretty_assertions::assert_eq;
use query::query_engine::DefaultSerializer;
use session::context::QueryContext;
use substrait::{DFLogicalSubstraitConvertor, SubstraitPlan};
use super::*;
use crate::test_utils::create_test_query_engine;
@@ -386,7 +495,7 @@ mod test {
// add update_at
(
"SELECT number FROM numbers_with_ts",
Ok("SELECT numbers_with_ts.number, now() FROM numbers_with_ts"),
Ok("SELECT numbers_with_ts.number, now() AS ts FROM numbers_with_ts"),
vec![
ColumnSchema::new("number", ConcreteDataType::int32_datatype(), true),
ColumnSchema::new(
@@ -400,7 +509,7 @@ mod test {
// add ts placeholder
(
"SELECT number FROM numbers_with_ts",
Ok("SELECT numbers_with_ts.number, 0 FROM numbers_with_ts"),
Ok("SELECT numbers_with_ts.number, CAST('1970-01-01 00:00:00' AS TIMESTAMP) AS __ts_placeholder FROM numbers_with_ts"),
vec![
ColumnSchema::new("number", ConcreteDataType::int32_datatype(), true),
ColumnSchema::new(
@@ -428,7 +537,7 @@ mod test {
// add update_at and ts placeholder
(
"SELECT number FROM numbers_with_ts",
Ok("SELECT numbers_with_ts.number, now(), 0 FROM numbers_with_ts"),
Ok("SELECT numbers_with_ts.number, now() AS update_at, CAST('1970-01-01 00:00:00' AS TIMESTAMP) AS __ts_placeholder FROM numbers_with_ts"),
vec![
ColumnSchema::new("number", ConcreteDataType::int32_datatype(), true),
ColumnSchema::new(
@@ -447,7 +556,7 @@ mod test {
// add ts placeholder
(
"SELECT number, ts FROM numbers_with_ts",
Ok("SELECT numbers_with_ts.number, numbers_with_ts.ts, 0 FROM numbers_with_ts"),
Ok("SELECT numbers_with_ts.number, numbers_with_ts.ts AS update_at, CAST('1970-01-01 00:00:00' AS TIMESTAMP) AS __ts_placeholder FROM numbers_with_ts"),
vec![
ColumnSchema::new("number", ConcreteDataType::int32_datatype(), true),
ColumnSchema::new(
@@ -466,7 +575,7 @@ mod test {
// add update_at after time index column
(
"SELECT number, ts FROM numbers_with_ts",
Ok("SELECT numbers_with_ts.number, numbers_with_ts.ts, now() FROM numbers_with_ts"),
Ok("SELECT numbers_with_ts.number, numbers_with_ts.ts, now() AS update_atat FROM numbers_with_ts"),
vec![
ColumnSchema::new("number", ConcreteDataType::int32_datatype(), true),
ColumnSchema::new(
@@ -528,8 +637,8 @@ mod test {
let query_engine = create_test_query_engine();
let ctx = QueryContext::arc();
for (before, after, column_schemas) in testcases {
let raw_schema = RawSchema::new(column_schemas);
let mut add_auto_column_rewriter = AddAutoColumnRewriter::new(raw_schema);
let schema = Arc::new(Schema::new(column_schemas));
let mut add_auto_column_rewriter = AddAutoColumnRewriter::new(schema);
let plan = sql_to_df_plan(ctx.clone(), query_engine.clone(), before, false)
.await
@@ -600,4 +709,18 @@ mod test {
);
}
}
#[tokio::test]
async fn test_null_cast() {
let query_engine = create_test_query_engine();
let ctx = QueryContext::arc();
let sql = "SELECT NULL::DOUBLE FROM numbers_with_ts";
let plan = sql_to_df_plan(ctx, query_engine.clone(), sql, false)
.await
.unwrap();
let _sub_plan = DFLogicalSubstraitConvertor {}
.encode(&plan, DefaultSerializer)
.unwrap();
}
}

View File

@@ -18,9 +18,9 @@
use std::collections::BTreeMap;
use hydroflow::scheduled::graph::Hydroflow;
use hydroflow::scheduled::graph_ext::GraphExt;
use hydroflow::scheduled::port::{PortCtx, SEND};
use dfir_rs::scheduled::graph::Dfir;
use dfir_rs::scheduled::graph_ext::GraphExt;
use dfir_rs::scheduled::port::{PortCtx, SEND};
use itertools::Itertools;
use snafu::OptionExt;
@@ -38,7 +38,7 @@ mod src_sink;
/// The Context for build a Operator with id of `GlobalId`
pub struct Context<'referred, 'df> {
pub id: GlobalId,
pub df: &'referred mut Hydroflow<'df>,
pub df: &'referred mut Dfir<'df>,
pub compute_state: &'referred mut DataflowState,
/// a list of all collections being used in the operator
///
@@ -361,16 +361,16 @@ mod test {
use std::cell::RefCell;
use std::rc::Rc;
use hydroflow::scheduled::graph::Hydroflow;
use hydroflow::scheduled::graph_ext::GraphExt;
use hydroflow::scheduled::handoff::VecHandoff;
use dfir_rs::scheduled::graph::Dfir;
use dfir_rs::scheduled::graph_ext::GraphExt;
use dfir_rs::scheduled::handoff::VecHandoff;
use pretty_assertions::assert_eq;
use super::*;
use crate::repr::Row;
pub fn run_and_check(
state: &mut DataflowState,
df: &mut Hydroflow,
df: &mut Dfir,
time_range: std::ops::Range<i64>,
expected: BTreeMap<i64, Vec<DiffRow>>,
output: Rc<RefCell<Vec<DiffRow>>>,
@@ -416,7 +416,7 @@ mod test {
}
pub fn harness_test_ctx<'r, 'h>(
df: &'r mut Hydroflow<'h>,
df: &'r mut Dfir<'h>,
state: &'r mut DataflowState,
) -> Context<'r, 'h> {
let err_collector = state.get_err_collector();
@@ -436,7 +436,7 @@ mod test {
/// that is it only emit once, not multiple times
#[test]
fn test_render_constant() {
let mut df = Hydroflow::new();
let mut df = Dfir::new();
let mut state = DataflowState::default();
let mut ctx = harness_test_ctx(&mut df, &mut state);
@@ -473,7 +473,7 @@ mod test {
/// a simple example to show how to use source and sink
#[test]
fn example_source_sink() {
let mut df = Hydroflow::new();
let mut df = Dfir::new();
let (send_port, recv_port) = df.make_edge::<_, VecHandoff<i32>>("test_handoff");
df.add_subgraph_source("test_handoff_source", send_port, move |_ctx, send| {
for i in 0..10 {
@@ -498,8 +498,8 @@ mod test {
#[test]
fn test_tee_auto_schedule() {
use hydroflow::scheduled::handoff::TeeingHandoff as Toff;
let mut df = Hydroflow::new();
use dfir_rs::scheduled::handoff::TeeingHandoff as Toff;
let mut df = Dfir::new();
let (send_port, recv_port) = df.make_edge::<_, Toff<i32>>("test_handoff");
let source = df.add_subgraph_source("test_handoff_source", send_port, move |_ctx, send| {
for i in 0..10 {

View File

@@ -14,8 +14,8 @@
use std::collections::BTreeMap;
use hydroflow::scheduled::graph_ext::GraphExt;
use hydroflow::scheduled::port::{PortCtx, SEND};
use dfir_rs::scheduled::graph_ext::GraphExt;
use dfir_rs::scheduled::port::{PortCtx, SEND};
use itertools::Itertools;
use snafu::OptionExt;
@@ -256,7 +256,7 @@ fn eval_mfp_core(
mod test {
use datatypes::data_type::ConcreteDataType;
use hydroflow::scheduled::graph::Hydroflow;
use dfir_rs::scheduled::graph::Dfir;
use super::*;
use crate::compute::render::test::{get_output_handle, harness_test_ctx, run_and_check};
@@ -269,7 +269,7 @@ mod test {
/// namely: if mfp operator can schedule a delete at the correct time
#[test]
fn test_render_mfp_with_temporal() {
let mut df = Hydroflow::new();
let mut df = Dfir::new();
let mut state = DataflowState::default();
let mut ctx = harness_test_ctx(&mut df, &mut state);
@@ -348,7 +348,7 @@ mod test {
/// that is it filter the rows correctly
#[test]
fn test_render_mfp() {
let mut df = Hydroflow::new();
let mut df = Dfir::new();
let mut state = DataflowState::default();
let mut ctx = harness_test_ctx(&mut df, &mut state);
@@ -388,7 +388,7 @@ mod test {
/// test if mfp operator can run multiple times within same tick
#[test]
fn test_render_mfp_multiple_times() {
let mut df = Hydroflow::new();
let mut df = Dfir::new();
let mut state = DataflowState::default();
let mut ctx = harness_test_ctx(&mut df, &mut state);

View File

@@ -22,7 +22,7 @@ use datatypes::data_type::ConcreteDataType;
use datatypes::prelude::DataType;
use datatypes::value::{ListValue, Value};
use datatypes::vectors::{BooleanVector, NullVector};
use hydroflow::scheduled::graph_ext::GraphExt;
use dfir_rs::scheduled::graph_ext::GraphExt;
use itertools::Itertools;
use snafu::{ensure, OptionExt, ResultExt};
@@ -1212,7 +1212,7 @@ mod test {
use common_time::Timestamp;
use datatypes::data_type::{ConcreteDataType, ConcreteDataType as CDT};
use hydroflow::scheduled::graph::Hydroflow;
use dfir_rs::scheduled::graph::Dfir;
use super::*;
use crate::compute::render::test::{get_output_handle, harness_test_ctx, run_and_check};
@@ -1228,7 +1228,7 @@ mod test {
/// expected: sum(number), window_start, window_end
#[test]
fn test_tumble_group_by() {
let mut df = Hydroflow::new();
let mut df = Dfir::new();
let mut state = DataflowState::default();
let mut ctx = harness_test_ctx(&mut df, &mut state);
const START: i64 = 1625097600000;
@@ -1389,7 +1389,7 @@ mod test {
/// select avg(number) from number;
#[test]
fn test_avg_eval() {
let mut df = Hydroflow::new();
let mut df = Dfir::new();
let mut state = DataflowState::default();
let mut ctx = harness_test_ctx(&mut df, &mut state);
@@ -1500,7 +1500,7 @@ mod test {
/// | col | Int64 |
#[test]
fn test_basic_distinct() {
let mut df = Hydroflow::new();
let mut df = Dfir::new();
let mut state = DataflowState::default();
let mut ctx = harness_test_ctx(&mut df, &mut state);
@@ -1556,7 +1556,7 @@ mod test {
/// | col | Int64 |
#[test]
fn test_basic_batch_reduce_accum() {
let mut df = Hydroflow::new();
let mut df = Dfir::new();
let mut state = DataflowState::default();
let now = state.current_time_ref();
let mut ctx = harness_test_ctx(&mut df, &mut state);
@@ -1662,7 +1662,7 @@ mod test {
/// | col | Int64 |
#[test]
fn test_basic_reduce_accum() {
let mut df = Hydroflow::new();
let mut df = Dfir::new();
let mut state = DataflowState::default();
let mut ctx = harness_test_ctx(&mut df, &mut state);
@@ -1739,7 +1739,7 @@ mod test {
/// this test include even more insert/delete case to cover all case for eval_distinct_core
#[test]
fn test_delete_reduce_distinct_accum() {
let mut df = Hydroflow::new();
let mut df = Dfir::new();
let mut state = DataflowState::default();
let mut ctx = harness_test_ctx(&mut df, &mut state);
@@ -1818,7 +1818,7 @@ mod test {
/// this test include insert and delete which should cover all case for eval_distinct_core
#[test]
fn test_basic_reduce_distinct_accum() {
let mut df = Hydroflow::new();
let mut df = Dfir::new();
let mut state = DataflowState::default();
let mut ctx = harness_test_ctx(&mut df, &mut state);
@@ -1896,7 +1896,7 @@ mod test {
/// | col | Int64 |
#[test]
fn test_composite_reduce_distinct_accum() {
let mut df = Hydroflow::new();
let mut df = Dfir::new();
let mut state = DataflowState::default();
let mut ctx = harness_test_ctx(&mut df, &mut state);

View File

@@ -17,7 +17,7 @@
use std::collections::BTreeMap;
use common_telemetry::{debug, trace};
use hydroflow::scheduled::graph_ext::GraphExt;
use dfir_rs::scheduled::graph_ext::GraphExt;
use itertools::Itertools;
use snafu::OptionExt;
use tokio::sync::broadcast::error::TryRecvError;

View File

@@ -16,16 +16,16 @@ use std::cell::RefCell;
use std::collections::{BTreeMap, VecDeque};
use std::rc::Rc;
use dfir_rs::scheduled::graph::Dfir;
use dfir_rs::scheduled::SubgraphId;
use get_size2::GetSize;
use hydroflow::scheduled::graph::Hydroflow;
use hydroflow::scheduled::SubgraphId;
use crate::compute::types::ErrCollector;
use crate::repr::{self, Timestamp};
use crate::utils::{ArrangeHandler, Arrangement};
/// input/output of a dataflow
/// One `ComputeState` manage the input/output/schedule of one `Hydroflow`
/// One `ComputeState` manage the input/output/schedule of one `Dfir`
#[derive(Debug, Default)]
pub struct DataflowState {
/// it is important to use a deque to maintain the order of subgraph here
@@ -38,7 +38,7 @@ pub struct DataflowState {
/// Which means it's also the current time in temporal filter to get current correct result
as_of: Rc<RefCell<Timestamp>>,
/// error collector local to this `ComputeState`,
/// useful for distinguishing errors from different `Hydroflow`
/// useful for distinguishing errors from different `Dfir`
err_collector: ErrCollector,
/// save all used arrange in this dataflow, since usually there is no delete operation
/// we can just keep track of all used arrange and schedule subgraph when they need to be updated
@@ -65,7 +65,7 @@ impl DataflowState {
/// schedule all subgraph that need to run with time <= `as_of` and run_available()
///
/// return true if any subgraph actually executed
pub fn run_available_with_schedule(&mut self, df: &mut Hydroflow) -> bool {
pub fn run_available_with_schedule(&mut self, df: &mut Dfir) -> bool {
// first split keys <= as_of into another map
let mut before = self
.schedule_subgraph

View File

@@ -18,10 +18,10 @@ use std::rc::Rc;
use std::sync::Arc;
use common_error::ext::ErrorExt;
use hydroflow::scheduled::graph::Hydroflow;
use hydroflow::scheduled::handoff::TeeingHandoff;
use hydroflow::scheduled::port::RecvPort;
use hydroflow::scheduled::SubgraphId;
use dfir_rs::scheduled::graph::Dfir;
use dfir_rs::scheduled::handoff::TeeingHandoff;
use dfir_rs::scheduled::port::RecvPort;
use dfir_rs::scheduled::SubgraphId;
use itertools::Itertools;
use tokio::sync::Mutex;
@@ -46,7 +46,7 @@ impl<T: 'static + Clone> Collection<T> {
/// clone a collection, require a mutable reference to the hydroflow instance
///
/// Note: need to be the same hydroflow instance that this collection is created from
pub fn clone(&self, df: &mut Hydroflow) -> Self {
pub fn clone(&self, df: &mut Dfir) -> Self {
Collection {
stream: self.stream.tee(df),
}
@@ -151,7 +151,7 @@ impl<T: 'static> CollectionBundle<T> {
}
impl<T: 'static + Clone> CollectionBundle<T> {
pub fn clone(&self, df: &mut Hydroflow) -> Self {
pub fn clone(&self, df: &mut Dfir) -> Self {
Self {
collection: self.collection.clone(df),
arranged: self

View File

@@ -25,7 +25,6 @@ use datafusion::config::ConfigOptions;
use datafusion::error::DataFusionError;
use datafusion::functions_aggregate::count::count_udaf;
use datafusion::functions_aggregate::sum::sum_udaf;
use datafusion::optimizer::analyzer::count_wildcard_rule::CountWildcardRule;
use datafusion::optimizer::analyzer::type_coercion::TypeCoercion;
use datafusion::optimizer::common_subexpr_eliminate::CommonSubexprEliminate;
use datafusion::optimizer::optimize_projections::OptimizeProjections;
@@ -42,6 +41,7 @@ use datafusion_expr::{
BinaryExpr, ColumnarValue, Expr, Operator, Projection, ScalarFunctionArgs, ScalarUDFImpl,
Signature, TypeSignature, Volatility,
};
use query::optimizer::count_wildcard::CountWildcardToTimeIndexRule;
use query::parser::QueryLanguageParser;
use query::query_engine::DefaultSerializer;
use query::QueryEngine;
@@ -61,9 +61,9 @@ pub async fn apply_df_optimizer(
) -> Result<datafusion_expr::LogicalPlan, Error> {
let cfg = ConfigOptions::new();
let analyzer = Analyzer::with_rules(vec![
Arc::new(CountWildcardRule::new()),
Arc::new(AvgExpandRule::new()),
Arc::new(TumbleExpandRule::new()),
Arc::new(CountWildcardToTimeIndexRule),
Arc::new(AvgExpandRule),
Arc::new(TumbleExpandRule),
Arc::new(CheckGroupByRule::new()),
Arc::new(TypeCoercion::new()),
]);
@@ -128,13 +128,7 @@ pub async fn sql_to_flow_plan(
}
#[derive(Debug)]
struct AvgExpandRule {}
impl AvgExpandRule {
pub fn new() -> Self {
Self {}
}
}
struct AvgExpandRule;
impl AnalyzerRule for AvgExpandRule {
fn analyze(
@@ -331,13 +325,7 @@ impl TreeNodeRewriter for ExpandAvgRewriter<'_> {
/// expand tumble in aggr expr to tumble_start and tumble_end with column name like `window_start`
#[derive(Debug)]
struct TumbleExpandRule {}
impl TumbleExpandRule {
pub fn new() -> Self {
Self {}
}
}
struct TumbleExpandRule;
impl AnalyzerRule for TumbleExpandRule {
fn analyze(

View File

@@ -49,6 +49,8 @@ pub trait FlowEngine {
async fn flush_flow(&self, flow_id: FlowId) -> Result<usize, Error>;
/// Check if the flow exists
async fn flow_exist(&self, flow_id: FlowId) -> Result<bool, Error>;
/// List all flows
async fn list_flows(&self) -> Result<impl IntoIterator<Item = FlowId>, Error>;
/// Handle the insert requests for the flow
async fn handle_flow_inserts(
&self,

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